{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2753.2955\n",
      "268.63927752610937\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "# 对数据进行标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.callbacks import EarlyStopping\n",
    "import MyTT as mt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pykalman import KalmanFilter\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
    "if gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用\n",
    "    tf.config.set_visible_devices([gpus[0]],\"GPU\")\n",
    "# 用于 reproducibility 的固定随机数种子，以便可重复性\n",
    "np.random.seed(42)\n",
    "data=pd.read_csv('data/训练用/2023-03-13 09_45_00.csv')\n",
    "data['close8']=data['close'].shift(-8)\n",
    "# 将特征和标签分离，由于最后一列为标签，因此将数据分离出来\n",
    "\n",
    "data.dropna(inplace=True)\n",
    "#X = data[['dkalman30','volume','STD26','ROC30']].values\n",
    "X = data[['noise_ratio','ATR10','CCI14','MACD','MTM10','ROC10','STD26','volume_ma10','dkalman30','ddkalman','volume_ma30','RSI20_MA5']].values\n",
    "y = data['close8'].values\n",
    "X_train = X[:8000]\n",
    "y_train = y[:8000]\n",
    "X_test = X[8000:9000]\n",
    "y_test = y[8000:9000]\n",
    "#print(data.isnull().sum())\n",
    "y_train_std=np.std(y_train.tolist())\n",
    "y_train_mean=np.mean(y_train.tolist())\n",
    "print(y_train_mean)\n",
    "print(y_train_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "113/113 [==============================] - 3s 12ms/step - loss: 1.0923 - val_loss: 0.2092\n",
      "Epoch 2/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 1.0931 - val_loss: 0.2381\n",
      "Epoch 3/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 1.0587 - val_loss: 0.1432\n",
      "Epoch 4/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.8541 - val_loss: 0.1164\n",
      "Epoch 5/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 1.0920 - val_loss: 0.1467\n",
      "Epoch 6/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.9377 - val_loss: 0.2095\n",
      "Epoch 7/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.8756 - val_loss: 0.2061\n",
      "Epoch 8/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7772 - val_loss: 0.2136\n",
      "Epoch 9/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7831 - val_loss: 0.2094\n",
      "Epoch 10/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.8101 - val_loss: 0.2011\n",
      "Epoch 11/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.9271 - val_loss: 0.2133\n",
      "Epoch 12/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7837 - val_loss: 0.3758\n",
      "Epoch 13/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.9531 - val_loss: 0.2091\n",
      "Epoch 14/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6859 - val_loss: 0.1208\n",
      "Epoch 15/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.9135 - val_loss: 0.2330\n",
      "Epoch 16/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.8660 - val_loss: 0.2581\n",
      "Epoch 17/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.8128 - val_loss: 0.2569\n",
      "Epoch 18/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.8475 - val_loss: 0.2501\n",
      "Epoch 19/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.8174 - val_loss: 0.2404\n",
      "Epoch 20/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7415 - val_loss: 0.2426\n",
      "Epoch 21/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7421 - val_loss: 0.2393\n",
      "Epoch 22/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7899 - val_loss: 0.2170\n",
      "Epoch 23/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7257 - val_loss: 0.2311\n",
      "Epoch 24/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7187 - val_loss: 0.3085\n",
      "Epoch 25/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7610 - val_loss: 0.2776\n",
      "Epoch 26/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6807 - val_loss: 0.2629\n",
      "Epoch 27/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6928 - val_loss: 0.2703\n",
      "Epoch 28/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6712 - val_loss: 0.2466\n",
      "Epoch 29/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6721 - val_loss: 0.2417\n",
      "Epoch 30/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6602 - val_loss: 0.2268\n",
      "Epoch 31/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6489 - val_loss: 0.2248\n",
      "Epoch 32/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7084 - val_loss: 0.3140\n",
      "Epoch 33/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.6843 - val_loss: 0.2769\n",
      "Epoch 34/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6734 - val_loss: 0.2709\n",
      "Epoch 35/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6309 - val_loss: 0.2521\n",
      "Epoch 36/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6444 - val_loss: 0.2463\n",
      "Epoch 37/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7766 - val_loss: 0.2522\n",
      "Epoch 38/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6184 - val_loss: 0.2405\n",
      "Epoch 39/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6118 - val_loss: 0.2349\n",
      "Epoch 40/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6477 - val_loss: 0.2320\n",
      "Epoch 41/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6725 - val_loss: 0.2305\n",
      "Epoch 42/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.6133 - val_loss: 0.2247\n",
      "Epoch 43/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6389 - val_loss: 0.2210\n",
      "Epoch 44/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6037 - val_loss: 0.2161\n",
      "Epoch 45/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.6436 - val_loss: 0.2157\n",
      "Epoch 46/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6099 - val_loss: 0.2132\n",
      "Epoch 47/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6012 - val_loss: 0.2090\n",
      "Epoch 48/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.7213 - val_loss: 0.2105\n",
      "Epoch 49/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.6372 - val_loss: 0.2083\n",
      "Epoch 50/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6557 - val_loss: 0.2029\n",
      "Epoch 51/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5700 - val_loss: 0.1048\n",
      "Epoch 52/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7055 - val_loss: 0.2047\n",
      "Epoch 53/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6939 - val_loss: 0.2059\n",
      "Epoch 54/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.7848 - val_loss: 0.2057\n",
      "Epoch 55/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6097 - val_loss: 0.1999\n",
      "Epoch 56/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6149 - val_loss: 0.1934\n",
      "Epoch 57/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5711 - val_loss: 0.1901\n",
      "Epoch 58/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5775 - val_loss: 0.1858\n",
      "Epoch 59/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5718 - val_loss: 0.1833\n",
      "Epoch 60/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.6175 - val_loss: 0.1773\n",
      "Epoch 61/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5496 - val_loss: 0.1719\n",
      "Epoch 62/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5722 - val_loss: 0.1700\n",
      "Epoch 63/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5479 - val_loss: 0.1667\n",
      "Epoch 64/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5526 - val_loss: 0.1626\n",
      "Epoch 65/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5563 - val_loss: 0.1604\n",
      "Epoch 66/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.6036 - val_loss: 0.1665\n",
      "Epoch 67/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.5641 - val_loss: 0.1619\n",
      "Epoch 68/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5675 - val_loss: 0.1596\n",
      "Epoch 69/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.5405 - val_loss: 0.1583\n",
      "Epoch 70/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5025 - val_loss: 0.1591\n",
      "Epoch 71/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5344 - val_loss: 0.1581\n",
      "Epoch 72/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.5243 - val_loss: 0.1551\n",
      "Epoch 73/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.5289 - val_loss: 0.1542\n",
      "Epoch 74/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5304 - val_loss: 0.1540\n",
      "Epoch 75/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.5156 - val_loss: 0.1531\n",
      "Epoch 76/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5057 - val_loss: 0.1509\n",
      "Epoch 77/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4907 - val_loss: 0.1508\n",
      "Epoch 78/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4926 - val_loss: 0.1509\n",
      "Epoch 79/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5069 - val_loss: 0.1491\n",
      "Epoch 80/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4928 - val_loss: 0.1457\n",
      "Epoch 81/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.5044 - val_loss: 0.1487\n",
      "Epoch 82/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4873 - val_loss: 0.1441\n",
      "Epoch 83/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5027 - val_loss: 0.1435\n",
      "Epoch 84/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4891 - val_loss: 0.1434\n",
      "Epoch 85/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4962 - val_loss: 0.1417\n",
      "Epoch 86/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4822 - val_loss: 0.1406\n",
      "Epoch 87/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4955 - val_loss: 0.1386\n",
      "Epoch 88/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4886 - val_loss: 0.1372\n",
      "Epoch 89/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4951 - val_loss: 0.1367\n",
      "Epoch 90/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4827 - val_loss: 0.1366\n",
      "Epoch 91/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4778 - val_loss: 0.1362\n",
      "Epoch 92/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4745 - val_loss: 0.1350\n",
      "Epoch 93/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5102 - val_loss: 0.1336\n",
      "Epoch 94/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4793 - val_loss: 0.1346\n",
      "Epoch 95/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5025 - val_loss: 0.1324\n",
      "Epoch 96/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4761 - val_loss: 0.1337\n",
      "Epoch 97/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4653 - val_loss: 0.1337\n",
      "Epoch 98/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4724 - val_loss: 0.1342\n",
      "Epoch 99/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4555 - val_loss: 0.1342\n",
      "Epoch 100/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4670 - val_loss: 0.1343\n",
      "Epoch 101/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4559 - val_loss: 0.1332\n",
      "Epoch 102/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4553 - val_loss: 0.1307\n",
      "Epoch 103/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4579 - val_loss: 0.1315\n",
      "Epoch 104/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4509 - val_loss: 0.1307\n",
      "Epoch 105/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4496 - val_loss: 0.1302\n",
      "Epoch 106/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4769 - val_loss: 0.1308\n",
      "Epoch 107/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4965 - val_loss: 0.1294\n",
      "Epoch 108/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4723 - val_loss: 0.1292\n",
      "Epoch 109/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4446 - val_loss: 0.1290\n",
      "Epoch 110/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4437 - val_loss: 0.1281\n",
      "Epoch 111/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4402 - val_loss: 0.1268\n",
      "Epoch 112/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4407 - val_loss: 0.1272\n",
      "Epoch 113/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4408 - val_loss: 0.1273\n",
      "Epoch 114/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4495 - val_loss: 0.1261\n",
      "Epoch 115/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4791 - val_loss: 0.1264\n",
      "Epoch 116/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4889 - val_loss: 0.1207\n",
      "Epoch 117/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4541 - val_loss: 0.1255\n",
      "Epoch 118/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4322 - val_loss: 0.1232\n",
      "Epoch 119/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4530 - val_loss: 0.1219\n",
      "Epoch 120/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4692 - val_loss: 0.1250\n",
      "Epoch 121/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4603 - val_loss: 0.1250\n",
      "Epoch 122/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4487 - val_loss: 0.1250\n",
      "Epoch 123/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4491 - val_loss: 0.1250\n",
      "Epoch 124/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4348 - val_loss: 0.1197\n",
      "Epoch 125/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4621 - val_loss: 0.1225\n",
      "Epoch 126/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4304 - val_loss: 0.1197\n",
      "Epoch 127/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4274 - val_loss: 0.1180\n",
      "Epoch 128/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4323 - val_loss: 0.1179\n",
      "Epoch 129/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4224 - val_loss: 0.1169\n",
      "Epoch 130/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4256 - val_loss: 0.1151\n",
      "Epoch 131/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4373 - val_loss: 0.1146\n",
      "Epoch 132/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4387 - val_loss: 0.1139\n",
      "Epoch 133/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4432 - val_loss: 0.1139\n",
      "Epoch 134/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4205 - val_loss: 0.1151\n",
      "Epoch 135/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4175 - val_loss: 0.1154\n",
      "Epoch 136/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4308 - val_loss: 0.1105\n",
      "Epoch 137/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4784 - val_loss: 0.1119\n",
      "Epoch 138/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4375 - val_loss: 0.1178\n",
      "Epoch 139/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4226 - val_loss: 0.1098\n",
      "Epoch 140/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4268 - val_loss: 0.1093\n",
      "Epoch 141/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4160 - val_loss: 0.1043\n",
      "Epoch 142/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4208 - val_loss: 0.1091\n",
      "Epoch 143/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4231 - val_loss: 0.1115\n",
      "Epoch 144/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4260 - val_loss: 0.1108\n",
      "Epoch 145/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4165 - val_loss: 0.1092\n",
      "Epoch 146/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4237 - val_loss: 0.1104\n",
      "Epoch 147/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4074 - val_loss: 0.1070\n",
      "Epoch 148/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4426 - val_loss: 0.1088\n",
      "Epoch 149/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4376 - val_loss: 0.1075\n",
      "Epoch 150/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4108 - val_loss: 0.1093\n",
      "Epoch 151/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4103 - val_loss: 0.1171\n",
      "Epoch 152/200\n",
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      "Epoch 153/200\n",
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      "Epoch 156/200\n",
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      "Epoch 157/200\n",
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      "Epoch 158/200\n",
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      "Epoch 159/200\n",
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      "Epoch 160/200\n",
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      "Epoch 161/200\n",
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      "Epoch 162/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4000 - val_loss: 0.1130\n",
      "Epoch 163/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4018 - val_loss: 0.1132\n",
      "Epoch 164/200\n",
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      "Epoch 165/200\n",
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      "Epoch 166/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4037 - val_loss: 0.1067\n",
      "Epoch 167/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4152 - val_loss: 0.1112\n",
      "Epoch 168/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4255 - val_loss: 0.1187\n",
      "Epoch 169/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4060 - val_loss: 0.1200\n",
      "Epoch 170/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3908 - val_loss: 0.1093\n",
      "Epoch 171/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4031 - val_loss: 0.1145\n",
      "Epoch 172/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4032 - val_loss: 0.1159\n",
      "Epoch 173/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3887 - val_loss: 0.1142\n",
      "Epoch 174/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3855 - val_loss: 0.1140\n",
      "Epoch 175/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3916 - val_loss: 0.1157\n",
      "Epoch 176/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3932 - val_loss: 0.1098\n",
      "Epoch 177/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3847 - val_loss: 0.1048\n",
      "Epoch 178/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3962 - val_loss: 0.1021\n",
      "Epoch 179/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4027 - val_loss: 0.1034\n",
      "Epoch 180/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4526 - val_loss: 0.1043\n",
      "Epoch 181/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.5123 - val_loss: 0.1020\n",
      "Epoch 182/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4069 - val_loss: 0.1035\n",
      "Epoch 183/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.4025 - val_loss: 0.1054\n",
      "Epoch 184/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3944 - val_loss: 0.1053\n",
      "Epoch 185/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3897 - val_loss: 0.1083\n",
      "Epoch 186/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3887 - val_loss: 0.1103\n",
      "Epoch 187/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3868 - val_loss: 0.1078\n",
      "Epoch 188/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3789 - val_loss: 0.1071\n",
      "Epoch 189/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3870 - val_loss: 0.1088\n",
      "Epoch 190/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3891 - val_loss: 0.1064\n",
      "Epoch 191/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3985 - val_loss: 0.1016\n",
      "Epoch 192/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3948 - val_loss: 0.1124\n",
      "Epoch 193/200\n",
      "113/113 [==============================] - 1s 8ms/step - loss: 0.3893 - val_loss: 0.1152\n",
      "Epoch 194/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3898 - val_loss: 0.1120\n",
      "Epoch 195/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3970 - val_loss: 0.1115\n",
      "Epoch 196/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.4315 - val_loss: 0.1134\n",
      "Epoch 197/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3847 - val_loss: 0.1095\n",
      "Epoch 198/200\n",
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      "Epoch 199/200\n",
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      "Epoch 200/200\n",
      "113/113 [==============================] - 1s 7ms/step - loss: 0.3727 - val_loss: 0.1122\n",
      "[[2667.0012]\n",
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      " [2820.9775]\n",
      " [2835.26  ]\n",
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      " [2778.881 ]\n",
      " [2738.68  ]\n",
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      " [2804.9092]\n",
      " [2791.5393]\n",
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      " [2780.5679]\n",
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      " [2792.8853]\n",
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      " [2656.9   ]\n",
      " [2661.6099]\n",
      " [2707.241 ]\n",
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      " [2698.386 ]\n",
      " [2707.7095]\n",
      " [2707.4944]\n",
      " [2704.893 ]\n",
      " [2683.6262]\n",
      " [2701.4248]\n",
      " [2702.5725]\n",
      " [2666.7808]\n",
      " [2691.1965]\n",
      " [2662.3818]\n",
      " [2664.435 ]\n",
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      " [2664.1167]\n",
      " [2702.508 ]\n",
      " [2662.9736]\n",
      " [2664.788 ]\n",
      " [2708.0852]\n",
      " [2707.5554]\n",
      " [2708.433 ]\n",
      " [2707.6675]\n",
      " [2708.1345]\n",
      " [2707.938 ]\n",
      " [2707.0671]\n",
      " [2706.2488]\n",
      " [2707.5134]\n",
      " [2706.7554]\n",
      " [2698.9436]\n",
      " [2707.043 ]\n",
      " [2707.9927]\n",
      " [2708.3884]\n",
      " [2708.2385]\n",
      " [2708.5908]\n",
      " [2707.73  ]\n",
      " [2667.425 ]\n",
      " [2667.384 ]\n",
      " [2707.5508]\n",
      " [2701.425 ]\n",
      " [2704.5554]\n",
      " [2666.592 ]\n",
      " [2660.2458]\n",
      " [2707.8115]\n",
      " [2697.7227]\n",
      " [2708.8726]\n",
      " [2702.652 ]\n",
      " [2707.2993]\n",
      " [2703.1338]\n",
      " [2704.9792]\n",
      " [2703.7551]\n",
      " [2699.4644]\n",
      " [2661.7039]\n",
      " [2707.2734]\n",
      " [2707.4302]\n",
      " [2707.82  ]\n",
      " [2708.8728]\n",
      " [2708.0835]\n",
      " [2707.416 ]\n",
      " [2706.7485]\n",
      " [2706.5706]\n",
      " [2708.5647]\n",
      " [2708.6948]\n",
      " [2708.6016]\n",
      " [2708.837 ]\n",
      " [2708.679 ]\n",
      " [2708.6343]\n",
      " [2708.812 ]\n",
      " [2708.4907]\n",
      " [2708.1667]\n",
      " [2707.7717]\n",
      " [2705.217 ]\n",
      " [2708.7686]\n",
      " [2708.6707]\n",
      " [2708.7825]\n",
      " [2707.6458]\n",
      " [2668.3223]\n",
      " [2706.2861]\n",
      " [2668.4292]\n",
      " [2667.9204]\n",
      " [2666.9185]\n",
      " [2708.3552]\n",
      " [2707.613 ]\n",
      " [2705.3691]\n",
      " [2661.4607]\n",
      " [2666.3599]\n",
      " [2663.3105]\n",
      " [2659.6191]\n",
      " [2665.729 ]\n",
      " [2665.7886]\n",
      " [2665.4578]\n",
      " [2660.0256]\n",
      " [2661.1118]\n",
      " [2665.9568]\n",
      " [2659.9954]\n",
      " [2657.9412]\n",
      " [2657.1633]\n",
      " [2657.902 ]\n",
      " [2658.1248]\n",
      " [2657.9766]\n",
      " [2656.8982]\n",
      " [2656.2014]\n",
      " [2656.266 ]\n",
      " [2656.7734]\n",
      " [2656.9753]\n",
      " [2657.0862]\n",
      " [2657.542 ]\n",
      " [2707.9358]\n",
      " [2708.8938]\n",
      " [2708.8125]\n",
      " [2708.9446]\n",
      " [2708.8982]\n",
      " [2708.92  ]\n",
      " [2708.9436]\n",
      " [2708.945 ]\n",
      " [2708.9458]\n",
      " [2708.9563]\n",
      " [2708.9116]\n",
      " [2708.8704]\n",
      " [2708.878 ]\n",
      " [2708.8757]\n",
      " [2708.76  ]\n",
      " [2708.836 ]\n",
      " [2708.8862]\n",
      " [2708.8777]\n",
      " [2708.8738]\n",
      " [2708.8877]\n",
      " [2708.8813]\n",
      " [2708.9033]\n",
      " [2708.8699]\n",
      " [2708.8306]\n",
      " [2708.7947]\n",
      " [2708.7437]\n",
      " [2708.8247]\n",
      " [2708.916 ]\n",
      " [2709.0203]\n",
      " [2708.9563]\n",
      " [2708.9194]\n",
      " [2708.9465]\n",
      " [2708.942 ]\n",
      " [2708.929 ]\n",
      " [2708.92  ]\n",
      " [2708.9111]\n",
      " [2708.8914]\n",
      " [2708.8708]\n",
      " [2709.048 ]\n",
      " [2709.0193]\n",
      " [2708.7083]]\n",
      "[2533. 2536. 2530. 2533. 2536. 2535. 2531. 2541. 2537. 2537. 2534. 2510.\n",
      " 2510. 2508. 2513. 2516. 2513. 2518. 2512. 2515. 2546. 2575. 2573. 2590.\n",
      " 2612. 2593. 2589. 2583. 2591. 2593. 2598. 2614. 2591. 2605. 2592. 2586.\n",
      " 2592. 2612. 2608. 2560. 2543. 2554. 2532. 2526. 2528. 2532. 2517. 2520.\n",
      " 2526. 2533. 2540. 2541. 2535. 2532. 2537. 2543. 2542. 2538. 2525. 2523.\n",
      " 2519. 2509. 2511. 2525. 2523. 2520. 2520. 2534. 2540. 2540. 2540. 2548.\n",
      " 2547. 2549. 2544. 2547. 2542. 2547. 2583. 2572. 2573. 2568. 2569. 2569.\n",
      " 2568. 2572. 2567. 2560. 2562. 2563. 2546. 2554. 2545. 2526. 2532. 2534.\n",
      " 2526. 2533. 2544. 2542. 2540. 2549. 2536. 2542. 2544. 2556. 2554. 2553.\n",
      " 2572. 2572. 2561. 2564. 2570. 2568. 2573. 2572. 2572. 2586. 2585. 2592.\n",
      " 2589. 2588. 2582. 2578. 2577. 2580. 2582. 2587. 2586. 2591. 2594. 2606.\n",
      " 2611. 2603. 2607. 2608. 2622. 2615. 2613. 2621. 2623. 2628. 2631. 2640.\n",
      " 2640. 2637. 2632. 2630. 2628. 2630. 2637. 2633. 2633. 2636. 2601. 2576.\n",
      " 2558. 2569. 2576. 2575. 2573. 2570. 2569. 2553. 2550. 2560. 2557. 2557.\n",
      " 2560. 2566. 2562. 2570. 2572. 2569. 2569. 2572. 2573. 2588. 2584. 2618.\n",
      " 2613. 2616. 2616. 2607. 2617. 2618. 2627. 2625. 2625. 2615. 2621. 2616.\n",
      " 2613. 2604. 2610. 2613. 2618. 2617. 2617. 2617. 2613. 2618. 2627. 2627.\n",
      " 2625. 2622. 2625. 2627. 2620. 2577. 2576. 2582. 2588. 2589. 2593. 2621.\n",
      " 2617. 2617. 2613. 2614. 2612. 2614. 2608. 2606. 2599. 2589. 2588. 2576.\n",
      " 2563. 2570. 2562. 2564. 2569. 2570. 2573. 2570. 2572. 2572. 2570. 2573.\n",
      " 2577. 2573. 2576. 2574. 2567. 2561. 2555. 2558. 2563. 2573. 2583. 2572.\n",
      " 2575. 2573. 2574. 2580. 2579. 2588. 2598. 2593. 2594. 2596. 2584. 2582.\n",
      " 2580. 2568. 2572. 2576. 2579. 2600. 2601. 2593. 2565. 2558. 2554. 2557.\n",
      " 2556. 2554. 2541. 2539. 2537. 2541. 2527. 2534. 2530. 2539. 2548. 2544.\n",
      " 2537. 2541. 2540. 2530. 2509. 2518. 2520. 2517. 2540. 2535. 2534. 2538.\n",
      " 2521. 2519. 2518. 2511. 2509. 2504. 2502. 2497. 2507. 2495. 2506. 2510.\n",
      " 2501. 2504. 2500. 2483. 2470. 2479. 2478. 2469. 2461. 2459. 2442. 2450.\n",
      " 2453. 2441. 2436. 2434. 2439. 2451. 2469. 2478. 2470. 2473. 2464. 2468.\n",
      " 2470. 2470. 2473. 2467. 2456. 2465. 2465. 2465. 2465. 2467. 2464. 2453.\n",
      " 2456. 2457. 2464. 2472. 2474. 2480. 2483. 2482. 2476. 2473. 2478. 2475.\n",
      " 2474. 2475. 2484. 2484. 2493. 2490. 2494. 2515. 2515. 2515. 2514. 2507.\n",
      " 2503. 2502. 2496. 2494. 2499. 2503. 2506. 2504. 2497. 2500. 2501. 2496.\n",
      " 2483. 2489. 2488. 2487. 2496. 2503. 2504. 2517. 2518. 2526. 2527. 2532.\n",
      " 2528. 2532. 2527. 2535. 2535. 2541. 2538. 2544. 2547. 2542. 2535. 2539.\n",
      " 2540. 2531. 2530. 2532. 2535. 2532. 2533. 2541. 2547. 2543. 2539. 2537.\n",
      " 2542. 2542. 2565. 2560. 2565. 2569. 2569. 2563. 2567. 2567. 2568. 2560.\n",
      " 2554. 2557. 2557. 2556. 2568. 2568. 2575. 2575. 2577. 2572. 2570. 2564.\n",
      " 2572. 2570. 2573. 2569. 2575. 2575. 2572. 2574. 2571. 2558. 2568. 2559.\n",
      " 2563. 2559. 2563. 2571. 2576. 2580. 2577. 2581. 2577. 2581. 2589. 2590.\n",
      " 2623. 2625. 2622. 2629. 2627. 2633. 2627. 2628. 2621. 2623. 2612. 2616.\n",
      " 2614. 2623. 2623. 2616. 2592. 2595. 2601. 2606. 2607. 2609. 2616. 2609.\n",
      " 2607. 2612. 2613. 2606. 2619. 2626. 2626. 2613. 2613. 2607. 2594. 2591.\n",
      " 2577. 2576. 2570. 2573. 2563. 2568. 2571. 2569. 2572. 2571. 2572. 2578.\n",
      " 2581. 2582. 2580. 2580. 2582. 2580. 2589. 2583. 2590. 2597. 2583. 2568.\n",
      " 2570. 2564. 2555. 2542. 2541. 2539. 2546. 2550. 2549. 2549. 2548. 2544.\n",
      " 2545. 2542. 2540. 2546. 2552. 2561. 2561. 2563. 2559. 2566. 2566. 2573.\n",
      " 2565. 2561. 2559. 2547. 2556. 2570. 2567. 2570. 2576. 2564. 2571. 2569.\n",
      " 2568. 2561. 2561. 2571. 2583. 2575. 2574. 2566. 2552. 2556. 2551. 2551.\n",
      " 2556. 2552. 2551. 2551. 2552. 2531. 2531. 2532. 2530. 2529. 2531. 2534.\n",
      " 2533. 2528. 2529. 2517. 2522. 2521. 2525. 2517. 2511. 2506. 2513. 2514.\n",
      " 2517. 2509. 2512. 2494. 2488. 2495. 2500. 2503. 2503. 2499. 2497. 2494.\n",
      " 2506. 2495. 2486. 2493. 2489. 2487. 2488. 2489. 2500. 2500. 2503. 2494.\n",
      " 2491. 2493. 2497. 2489. 2498. 2500. 2512. 2510. 2515. 2517. 2508. 2511.\n",
      " 2507. 2516. 2516. 2520. 2533. 2529. 2531. 2541. 2537. 2544. 2546. 2553.\n",
      " 2552. 2546. 2548. 2550. 2550. 2547. 2544. 2544. 2555. 2551. 2549. 2547.\n",
      " 2553. 2551. 2553. 2560. 2562. 2570. 2565. 2569. 2568. 2564. 2563. 2567.\n",
      " 2573. 2569. 2566. 2563. 2559. 2557. 2557. 2565. 2564. 2558. 2556. 2565.\n",
      " 2558. 2556. 2554. 2556. 2558. 2564. 2572. 2567. 2570. 2569. 2569. 2565.\n",
      " 2567. 2568. 2574. 2567. 2574. 2575. 2571. 2572. 2578. 2577. 2587. 2591.\n",
      " 2590. 2590. 2592. 2594. 2600. 2609. 2614. 2629. 2628. 2635. 2645. 2651.\n",
      " 2653. 2632. 2630. 2636. 2640. 2660. 2660. 2663. 2666. 2667. 2681. 2678.\n",
      " 2668. 2661. 2665. 2669. 2668. 2664. 2665. 2663. 2658. 2659. 2662. 2661.\n",
      " 2658. 2637. 2642. 2647. 2664. 2662. 2638. 2644. 2641. 2637. 2638. 2635.\n",
      " 2634. 2637. 2638. 2633. 2638. 2631. 2630. 2630. 2629. 2621. 2621. 2618.\n",
      " 2617. 2629. 2630. 2630. 2634. 2632. 2626. 2623. 2630. 2636. 2635. 2630.\n",
      " 2629. 2630. 2630. 2627. 2623. 2616. 2615. 2617. 2613. 2603. 2615. 2609.\n",
      " 2600. 2599. 2617. 2618. 2626. 2627. 2628. 2625. 2619. 2619. 2625. 2632.\n",
      " 2632. 2616. 2606. 2620. 2623. 2629. 2625. 2616. 2618. 2610. 2612. 2607.\n",
      " 2592. 2595. 2600. 2584. 2585. 2573. 2578. 2582. 2587. 2584. 2588. 2597.\n",
      " 2605. 2610. 2604. 2603. 2609. 2604. 2605. 2606. 2602. 2596. 2591. 2598.\n",
      " 2587. 2585. 2595. 2610. 2622. 2622. 2610. 2613. 2628. 2626. 2624. 2618.\n",
      " 2621. 2626. 2620. 2622. 2619. 2623. 2619. 2623. 2628. 2623. 2625. 2633.\n",
      " 2629. 2639. 2636. 2642. 2642. 2638. 2637. 2642. 2640. 2634. 2641. 2645.\n",
      " 2647. 2646. 2648. 2646. 2636. 2635. 2648. 2641. 2644. 2633. 2610. 2629.\n",
      " 2616. 2625. 2616. 2621. 2618. 2617. 2619. 2624. 2622. 2643. 2642. 2647.\n",
      " 2662. 2654. 2648. 2645. 2644. 2655. 2658. 2659. 2664. 2656. 2656. 2658.\n",
      " 2654. 2651. 2652. 2646. 2655. 2652. 2652. 2653. 2644. 2649. 2647. 2646.\n",
      " 2645. 2660. 2655. 2654. 2627. 2620. 2617. 2615. 2621. 2624. 2622. 2617.\n",
      " 2621. 2628. 2627. 2624. 2621. 2624. 2625. 2624. 2620. 2617. 2616. 2619.\n",
      " 2620. 2621. 2623. 2650. 2674. 2668. 2675. 2674. 2678. 2680. 2679. 2679.\n",
      " 2683. 2684. 2692. 2691. 2689. 2680. 2682. 2691. 2688. 2690. 2693. 2690.\n",
      " 2692. 2688. 2684. 2684. 2679. 2683. 2692. 2696. 2697. 2688. 2693. 2695.\n",
      " 2694. 2696. 2697. 2695. 2693. 2718. 2721. 2722. 2722. 2723. 2726. 2737.\n",
      " 2727. 2696. 2712. 2706.]\n",
      "Test MSE: 13641.066\n"
     ]
    }
   ],
   "source": [
    "\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_y = StandardScaler()\n",
    "y_train = sc_y.fit_transform(y_train.reshape(-1, 1))\n",
    "\n",
    "\n",
    "# 将数据整理为适合 LSTM 模型的维度和格式\n",
    "# 将数据整理为三维矩阵（记录数、时间步数、特征数）\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "# 填写模型的结构参数\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=200, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=100,return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "# 编译模型，指定优化方法和损失函数\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# 训练 LSTM 模型，使用 Mini-Batch 方法，采用早停机制\n",
    "model.fit(X_train, y_train, batch_size=64, epochs=200, validation_split=0.1, shuffle=False)\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=5)\n",
    "# 在测试集上进行准确度测试\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = sc_y.inverse_transform(y_pred)\n",
    "\n",
    "# 基于MSE提供测试精度\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(y_pred)\n",
    "print(y_test)\n",
    "print('Test MSE: %.3f' % mse)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "-3.406669452309213"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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URaxZswaZmZlQq9UAgNGjRyM5ORkFBQVoaGhAe3s7Xn75ZWi1WtTU1AAAtFotQkJCMHjwYLPtxcXFQavVmtoolco+76lUKk1teissLDTlBwmCgKSkJEcPjYiIiPyYw8FPXl4eTp06hXfeecf0WHBwMN5//3188cUXiImJQUREBA4fPoy5c+ciMDCw3+2JogiFQmH6vee/rbXpqaCgADqdzvTz7bffOnhkRERE5M+CHHlRfn4+ioqKcOTIESQmJpo9N2nSJFRWVkKn06G9vR1Dhw7F1KlTMXnyZACASqVCe3s7GhoazHp/6urqMG3aNFOb2traPu977do1xMXFWdyn0NBQhIaGOnI4REREJCN29fyIooi8vDzs3r0bBw8eREpKitW2giBg6NChuHDhAj777DMsXLgQQHdwFBwcjOLiYlPbmpoaVFVVmYKfjIwM6HQ6HD9+3NTm2LFj0Ol0pjZEREREjrBrttdTTz2FnTt3Yu/evWYzrgRBQHh4OADgvffew9ChQzFs2DCcPn0aTz/9NCZNmoT333/f1H7FihX44IMP8NZbbyEmJgbr1q3DjRs3cOLECdPw2Ny5c3H16lVs2bIFALB8+XIkJydj3759kvaVs72IiIgGHk/cv+0a9tq8eTMAYObMmWaPb926FcuWLQPQ3YuzZs0a1NbWIj4+Hv/6r/+K9evXm7V/5ZVXEBQUhIceegi3bt3Cfffdh7feesssL2jHjh1YtWqVaVbYggULsGnTJnuPj4iIiMiMU3V+fBl7foiIiAYen6/zQ0RERDTQMPghIiIiWWHwQ0RERLLC4IeIiIhkhcEPERERyQqDHyIiIpIVBj9EREQkKwx+iIiISFYY/BAREZGsMPghIiIiWWHwQ0RERLLC4IeIiIhkhcEPERERyQqDHyIiIpIVBj9EREQkKwx+iIiISFYY/BAREZGsMPghIiIiWWHwQ0RERLLC4IeIiIhkhcEPERERyQqDHyIiIpIVBj9EREQkKwx+iIiISFYY/BAREZGsMPghIiIiWWHwQ0RERLLC4IeIiIhkhcEPERERyQqDHyIiIpKVIG/vABEREQB0GUQcv1SPuqZWKKPCcE9KDAIDFN7eLfJDDH6IiMjrNFU1eGFfNWp0rabH4oUwbHgwFdnqeC/uGfkjDnsREZFXaapqsGJ7hVngAwBaXStWbK+ApqrGS3tG/orBDxEReU2XQcQL+6ohWnjO+NgL+6rRZbDUgsgxDH6IiMhrjl+q79Pj05MIoEbXiuOX6j23U+T37Ap+CgsLMWXKFERFRUGpVGLRokU4f/68WZvm5mbk5eUhMTER4eHhGDNmDDZv3mzWZubMmVAoFGY/jzzyiFmbhoYG5OTkQBAECIKAnJwcNDY2OnaURETkk+qarAc+jrQjksKu4KekpAQrV65EeXk5iouL0dnZiaysLLS0tJjarF69GhqNBtu3b8fZs2exevVq5OfnY+/evWbbys3NRU1Njelny5YtZs8vWbIElZWV0Gg00Gg0qKysRE5OjhOHSkREvkYZFebSdkRS2DXbS6PRmP2+detWKJVKnDhxAjNmzAAAlJWV4fHHH8fMmTMBAMuXL8eWLVvw2WefYeHChabXRkREQKVSWXyfs2fPQqPRoLy8HFOnTgUAvP7668jIyMD58+cxatQoe3abiIh81D0pMYgXwqDVtVrM+1EAUAnd096JXMWpnB+dTgcAiIn54aLMzMxEUVERvvvuO4iiiEOHDuGLL77AnDlzzF67Y8cOxMbGYuzYsVi3bh2amppMz5WVlUEQBFPgAwDp6ekQBAGlpaUW96WtrQ16vd7sh4iIfFtggAIbHkwF0B3o9GT8fcODqaz3Qy7lcPAjiiLWrFmDzMxMqNVq0+N//vOfkZqaisTERISEhCA7OxuvvvoqMjMzTW2WLl2Kd955B4cPH8b69evx/vvvY/HixabntVotlEpln/dUKpXQarUW96ewsNCUHyQIApKSkhw9NCIi8qBsdTw2P5YGlWA+tKUSwrD5sTTW+SGXc7jIYV5eHk6dOoWjR4+aPf7nP/8Z5eXlKCoqQnJyMo4cOYKnnnoK8fHxmD17NoDufB8jtVqNkSNHYvLkyaioqEBaWhoAQKHoG+WLomjxcQAoKCjAmjVrTL/r9XoGQEREA0S2Oh73p6pY4Zk8wqHgJz8/H0VFRThy5AgSExNNj9+6dQvPPfcc9uzZgwceeAAAMH78eFRWVuL3v/+9KfjpLS0tDcHBwbhw4QLS0tKgUqlQW1vbp921a9cQFxdncRuhoaEIDQ115HCIiMgHBAYokDFiiLd3g2TArmEvURSRl5eH3bt34+DBg0hJSTF7vqOjAx0dHQgIMN9sYGAgDAaD1e2eOXMGHR0diI/v7trMyMiATqfD8ePHTW2OHTsGnU6HadOm2bPLRERERGbs6vlZuXIldu7cib179yIqKsqUfyMIAsLDwxEdHY17770Xzz77LMLDw5GcnIySkhL813/9F/74xz8CAC5evIgdO3Zg3rx5iI2NRXV1NdauXYu7774b06dPBwCMGTMG2dnZyM3NNU2BX758OebPn8+ZXiRbXPSRiMg1FKIoSq4Zbi3fZuvWrVi2bBmA7mTlgoICfPTRR6ivr0dycjKWL1+O1atXQ6FQ4Ntvv8Vjjz2GqqoqNDc3IykpCQ888AA2bNhgNmusvr4eq1atQlFREQBgwYIF2LRpEwYNGiRpX/V6PQRBgE6nQ3R0tNRDJPJJXPSRiOTCE/dvu4KfgYTBD/kL46KPvf9QjV9FOBuGiPyJJ+7fXNuLyIdx0UciItdj8EPkw7joIxGR6zlc54eI3I+LPpK/YyI/eQODHyIfxkUfyZ8xkZ+8hcNeRD6soaXNZpt4LvpIA5Axkb/3sK5W14oV2yugqarx0p6RHDD4IfJRXQYRL+4/a7Pd+ge46CMNLEzkJ29j8EPko2wlOxsNjgzxwN4QuQ4T+cnbmPND5KOY7Ez+Suo1q9XdQtnFG0yGJpdj8EPko5jsTP5K6jX74v6zqG9pN/3OZGhyFQ57Efmoe1JiEC+Ewdr3XAWY7EwDk/HatqVn4AMwGZpch8EPkY8KDFBgw4OpANAnADL+vuFBJjvTwBMYoMD6B8bY/TomQ5OrMPgh8mHZ6nhsfiwNql7fklVCGNf0ogFtcGSoQ69jMjS5AnN+iHxctjoe96eqWAWX/IqzifpM9CdnMPghGgACAxTIGDHE27tB5DLOJupfvt7ioj0hOeKwFxEReZythH5b3jn+DfN+yGEMfoiIyCO6DCLKLt7A3srvcPxSPdY/YDmhXwqtvo15P+QwDnsREZHbWVvEdPmMFBSdrDF7fFBEMBpvdtjcJvN+yFEMfoiIyK2Mi5j2HqTS6lrxtyOX8Ncld2NwZKgpod9gELH0jWM2t8sCn+QoBj9EROQ2thYxVaC7kvPRX84yzWDsMoiIF8Kg1bVafJ0C3eUeWOCTHMWcHyIichtHFjFlgU9yNwY/RETkNo4u0MsCn+ROHPYiIiK3cWaBXhb4JHdh8ENERG5jrOfjaP4OC3ySO3DYi8gH9Kx/UnbxBou3kd9g/g75Ivb8EHmZtfonGx5MZV4D+QVj/k7v6zwiNBD3joxFVGgwugwiAyDyGIUoin75FVOv10MQBOh0OkRHR3t7d4gsslb/xHgLYGIn+ZMug4hNB7/EliMXcbO9y+y5QRHBeHnxOF7v5JH7N4e9iLzEVv0TAHhhXzWHwMhvFFdr8cqBL/oEPgDQeLMDT26vgKaqxgt7RnLD4IfISxypf0I0UHUZRDxfVG2z3fNFZxjwk9sx+CHyEkfrnxANRMcv1UOrt30tc8FS8gQGP0Re4kz9E6KBxp4gngE/uRuDHyIvMdY/sTa/RYHuWV9cv4gGMmMZhwu1zZJfw4Cf3I1T3Ym8xFj/ZMX2CigAs8Rn1j8hf2CpjIMtquhQBvzkduz5IfIirl9E/spYxsGewAcAnl8wlgE/uR17fohcrMsg2rUWEdcvIn/TXxkHa1jnhzyJwQ+RCzlarZnrF5E/sVXGwWjRxAQkDg5Hxh2xSB8xhAE/eQyDHyIXsVatWatrxYrtFRzGItmQOltrSGQI1s0ZbfaYvT2nRI6wK+ensLAQU6ZMQVRUFJRKJRYtWoTz58+btWlubkZeXh4SExMRHh6OMWPGYPPmzWZt2trakJ+fj9jYWERGRmLBggW4cuWKWZuGhgbk5ORAEAQIgoCcnBw0NjY6dpREbsZqzUQ/kDpb641/Xjar6KypqkHmxoN49PVyPL2rEo++Xo7MjQdZ9Zlczq7gp6SkBCtXrkR5eTmKi4vR2dmJrKwstLS0mNqsXr0aGo0G27dvx9mzZ7F69Wrk5+dj7969pjbPPPMM9uzZg127duHo0aNobm7G/Pnz0dX1Q8nzJUuWoLKyEhqNBhqNBpWVlcjJyXHBIRO5Hqs1E/3AVhmHnoxfCqwlSBt7ThkAkSs5tbDptWvXoFQqUVJSghkzZgAA1Go1Hn74Yaxfv97UbtKkSZg3bx5efPFF6HQ6DB06FNu2bcPDDz8MALh69SqSkpLw4YcfYs6cOTh79ixSU1NRXl6OqVOnAgDKy8uRkZGBc+fOYdSoUTb3jQubkiftrfwOT++qtNnuT49MxMKJt7t/h4i8TFNVgye3V0hqu+NnU7HuvZNWv0Ao0D0D8ugvZ3EITAZ8fmFTnU4HAIiJ+aEmQ2ZmJoqKivDdd99BFEUcOnQIX3zxBebMmQMAOHHiBDo6OpCVlWV6TUJCAtRqNUpLSwEAZWVlEATBFPgAQHp6OgRBMLXpra2tDXq93uyHyFNYrZnIXLY6Hv82fbiktmUXb7DnlDzK4eBHFEWsWbMGmZmZUKvVpsf//Oc/IzU1FYmJiQgJCUF2djZeffVVZGZmAgC0Wi1CQkIwePBgs+3FxcVBq9Wa2iiVyj7vqVQqTW16KywsNOUHCYKApKQkRw+NyG6s1kz+yliheW/ldyi7eMOuvLX7U1USW0rbJpe9IFdxeLZXXl4eTp06haNHj5o9/uc//xnl5eUoKipCcnIyjhw5gqeeegrx8fGYPXu21e2JogiF4odbR89/W2vTU0FBAdasWWP6Xa/XMwAij2G1ZvIVrpwt5WjpBiPjlwKtrtVieGMczsq4IxabDl20uT32nJKrOBT85Ofno6ioCEeOHEFiYqLp8Vu3buG5557Dnj178MADDwAAxo8fj8rKSvz+97/H7NmzoVKp0N7ejoaGBrPen7q6OkybNg0AoFKpUFtb2+d9r127hri4OIv7FBoaitDQUEcOh8gljNWae98sVHbcLIic4UiwYi1YckXpBqlfCtJHDJEUJLHnlFzFruBHFEXk5+djz549OHz4MFJSUsye7+joQEdHBwICzEfTAgMDYTAYAHQnPwcHB6O4uBgPPfQQAKCmpgZVVVX47W9/CwDIyMiATqfD8ePHcc899wAAjh07Bp1OZwqQiHwRqzWTt/QXrDy5vQL/Jy0REaGBSI6JQE7GcIQEBVgNltY/MAYv7j9rtXSDAt2ztO5PVdm8tqV+KWDPKXmSXbO9nnrqKezcuRN79+41m3ElCALCw8MBADNnzsT169exadMmJCcno6SkBCtWrMAf//hHrFixAgCwYsUKfPDBB3jrrbcQExODdevW4caNGzhx4gQCAwMBAHPnzsXVq1exZcsWAMDy5cuRnJyMffv2SdpXzvYiIrnoMojI3HhQ8jpaAQrgvjFKHKiu6xPg9A4++vNObrrkyuRShuOcHWYj/+CJ+7ddwY+1fJutW7di2bJlALqTlQsKCvDRRx+hvr4eycnJWL58OVavXm16fWtrK5599lns3LkTt27dwn333YdXX33VLEenvr4eq1atQlFREQBgwYIF2LRpEwYNGiRpXxn8EJFclF28gUdfL/f4+7qjdAMrPJPPBT8DCYMfIpILqXWmXM2enh8iqTxx/+baXkREA5ynZ0ExAZkGOqeKHBIRkffZs5yEvXpvkwnI5A8Y/BARDXDGKeVA32DFEcainK8uSYNKMO9VUglhkqa52+JM8UQiZzHnh4hkQQ6JtJZmS9nLeEaMAY47zhtndVF/mPDsBAY/RGQkp5ttz2Bl17FvUGbneljuPi/W6hH1DrpIvhj8OIHBDxEB8r7ZdhlEjHv+f3Gzvcvi88bE5d//nwm43tLm9h4xW/WIuHo7AQNgVXciIl/WZRDxwr5qq5WKge5Kxf6abxIYoMAfH5pg8bmeicvTR8Zi4cTbkTFiiFuDjuOX6iWt3l7+1Q237QMRwOCHiPyY1JvtcTuHhgaSbHU8XnssDfFuSly2h9RV2VfuqICmqsbNe0Nyxjo/ROS3pN5spbYbqHxlzTmp9Ygab3VIXjyVyBEMfojIb0m92Xq6SKC79DczKzBA4fVqzMZ6RNZWb+9JhPTFU4nsxeCHiPxSl0GEwSBiUHgwGm91WGzjT5WKB8KMNmM9ohXbKyS1Nw5JejtoI//DnB8i8juaqhpkbjyIpW8c6zfwAfyjUrFxRlvv/CatrhUrtnfnz/hKUcFsdTyemX2X5Pb+PiRJ3sGeHyLyK9amtvem8rFeEUfZmtGmAPCr3afxfFE1tHrf6BUaHhshua2/DEmSb2HwQ0R+o79AwGhQeDD+ujQN6Xe4d1q3p0iZ0dZ4swOAeQ+YsVfIG0nFUgOaIZEhfjEkSb6Hw15E5DdsBQJA90yiAIXCLwIfwPFhIW/WOTImPtvy4kK13/w/kW9h8ENEfkOOU9udGRbyVp0jY+Jzf2HNz2ekYI5a5RN5SuR/OOxFRH5DblPbAfumj1tjDAY9ufhrtjoemx9L6zNDLSYyGC8tVCMgQNFnKQxfm71GAxeDHyLyG7YCAX+a2m7Uc/q4AnAoAFJGhXllqry14ovF1VqLSevezFMi/8JhLyLyG8ZAAECfIRV/mtrem7EXRdV7CYvoUAyKCLY6vKRAd4DT0NJmc6q8uxiLLxrXFgMg6/XYyDPY80PkBZ4cXpAba8Mp/jK13RpbvSi9e4WMV9v6B1Lx4v7+p8p7stKyPeuxsfghOYrBD5GHDYRKvAOdr6xl5WmWlrCwFQwK4SE+FWzIMWmdPI/BD5EHWSvAZ28uA3uObPOFtax8RX/B4N7K7yRtwxPBRpdBxPWmNklt/SlpnTyPwQ+Rh0ipxCtleIE9R+QIa8Ggr8yQs3RdW+KPSevkeUx4JvIQe3IZrJGyhhORPYwz5GwlRbsz2LB2XVvaF8A/k9bJsxj8EHmIs7kMXQYRzxedGRCzYHxlEU2yzdsz5KQsSWKkEsI4zZ1cgsNeRB7i7PDCpoNfQqu3ng/hK7NgOCw38HhzhpyUJUkAYP0DY7Bsegp7fMglGPwQeYgzBfg0VTV45cAXkt7Hm7NgXJXQTZ7nrRlyUq/X2KhQBj7kMhz2IvIQR4cXjMMCUnlrFoythG7Ad4blyLLeBQc9EWz4SsI1yQuDHyIPslqJt59cBqnDAoD7E1P744qEbpIfX0i4JvnhsBeRhxmHF8q/uoGyizcAiMi4IxbpVvJ07BnG8uYsGBanI0f0tzYZZ3eRuzD4IfKC4mqtWXLppkMXrSYFS+3uXz17pFfzaTh84R3+UPBSrkuSkPcw+CHyMHuTgm0lSgPdwwJ5s0a6bZ+lkOOK6t7m7Mw6Xwqc5LokCXmHQhRFv8w+1Ov1EAQBOp0O0dHR3t4dIgDdN5vMjQet5sYYA4Sjv5xl9qFvDJgAy8MCvjKLaqDspz+wFkRLPdcsSUC+yhP3byY8E3mQ1KTgP3x03qw4oCOJ0t4wUPZzoHN2Zh0rhZPccdiLyIOkJvu+evgiXj1sngc0UIYFBsp+DmT2zKzrXfDSVWvMEQ1kdvX8FBYWYsqUKYiKioJSqcSiRYtw/vx5szYKhcLiz+9+9ztTm5kzZ/Z5/pFHHjHbTkNDA3JyciAIAgRBQE5ODhobGx0/UiIfYG+yb42uFU/2+CbujTosjhgo+zlQOTOzjiUJiOwMfkpKSrBy5UqUl5ejuLgYnZ2dyMrKQktLi6lNTU2N2c+bb74JhUKBn/70p2bbys3NNWu3ZcsWs+eXLFmCyspKaDQaaDQaVFZWIicnx4lDJfI+WzVNrPnV7tMsDkgmzsysY0kCIjuHvTQajdnvW7duhVKpxIkTJzBjxgwAgEqlMmuzd+9e/PjHP8Ydd9xh9nhERESftkZnz56FRqNBeXk5pk6dCgB4/fXXkZGRgfPnz2PUqFH27DaRz+hZ08QejTc7sOngl3h6tndmdPnSrCBybmYdSxIQOZnwrNPpAAAxMZanrtbW1mL//v144okn+jy3Y8cOxMbGYuzYsVi3bh2amppMz5WVlUEQBFPgAwDp6ekQBAGlpaUW36utrQ16vd7sh8gXGZOCB4UH2/W6raWXPNr7Y1yZ/f/tO4Mpvz6AR18vx9O7KvHo6+XI3HgQmqoart7uJc6sxO4LFZV53ZC3OZzwLIoi1qxZg8zMTKjVaott3n77bURFRWHx4sVmjy9duhQpKSlQqVSoqqpCQUEBTp48ieLiYgCAVquFUqnssz2lUgmtVmvxvQoLC/HCCy84ejhEHpWtjkdUWDCW/ucxya9pvNnhsRXbLU2D7kn7fS7SoIhgNN7sMD3OqdKe42hhQG9XVOYUe/IFDgc/eXl5OHXqFI4ePWq1zZtvvomlS5ciLMy8+zQ3N9f0b7VajZEjR2Ly5MmoqKhAWloagO7E6d5EUbT4OAAUFBRgzZo1pt/1ej2SkpLsOiYiW1w5/JN+xxCbxQt7c2cehvHYiqu1ePOfl/tta9zfnoEPIL/V2+25HtwxdOjozDpvVVS2t8Ankbs4FPzk5+ejqKgIR44cQWJiosU2n3zyCc6fP493333X5vbS0tIQHByMCxcuIC0tDSqVCrW1tX3aXbt2DXFxcRa3ERoaitDQUPsOhMgO7qima2/+j7vyMGz19Eglp6nS9lwP7uztMM6ss5enSxJwij35ErtyfkRRRF5eHnbv3o2DBw8iJSXFats33ngDkyZNwoQJE2xu98yZM+jo6EB8fPeHQEZGBnQ6HY4fP25qc+zYMeh0OkybNs2eXSZyCWeLwmmqapC58aBZ3syUXxfDYBC7iwJG9x+4O5qHISW3wtqxOUoOU6XtuR7svXY8mQ/jyZIEnGJPvsSunp+VK1di586d2Lt3L6Kiokz5N4IgIDw83NROr9fjvffewx/+8Ic+27h48SJ27NiBefPmITY2FtXV1Vi7di3uvvtuTJ8+HQAwZswYZGdnIzc31zQFfvny5Zg/fz5nepHHOfuN1VpXf31LB57a+Tl+PiMF//zVfdh08Eu8cuCLPq93NA9DSm9Df8fmLH+dKm3P9YDv/y312vHnfBhOsSdfYlfPz+bNm6HT6TBz5kzEx8ebfnoPbe3atQuiKOLRRx/ts42QkBB8/PHHmDNnDkaNGoVVq1YhKysLBw4cQGBgoKndjh07MG7cOGRlZSErKwvjx4/Htm3bHDxMIsc5841VSnCx5cgl/G+VFk/PHonXHktDvAuWhpDa22Dr2Jzhr1Ol7bke7Gnrr0tOGHuyLtQ2S2rvruuGM8yoJ7t6fqSugbp8+XIsX77c4nNJSUkoKSmxuY2YmBhs377dnt0jcgt3VtM1Wr+3CnPUKpfkYUjpmfj3PVW41d6FL+uk3ZDs4e+rt7ujB0Orb8VvNef8Lh/Gnlwyd143/tyjRo7h2l5ENniimu6NlnbTNHZHE1iNpPQ23Ghpx+r/Punwe9wWGoTmtk6vTJX2NncUCaxvbnN4rS5fZW241xJ3XjecYUaWcFV3IhucKQpnzw3QVbkOnsiZeHHhWLwm09Xb7bkepLaNiQyR9N4DJR/G3lwyd103tnpBge4eNQ6ByQ97fohscKYo3D0pMYiJDEZ9S0ef53pzVa6DJ3JtVEI4MkYMkeXq7fZeD1LaCuHSgp+Bkkcldbg378cjMP3OoW67buzJuRooPWrkGuz5IZLAWBTO3p6OwAAFXlpouQJ6T65cTsDRxVOl6N3LJdfV2+25HqS0daR30d4EXk8m/ErtoRoZF2X1unHF/nKGGVnDnh8iiRxNRp43PgE/v9KILUcuWXxeAdfmOvTXM+EMOeTz2MOe6yFbHY9Zo+Owrewyvq6/ieSYCORkDEdIUPf3T+P/2ZNWCl6KMD/vUssYGPft8vUWvHP8G2j1bVbbu5KzeVGuSFDuMoi43tRmu2E/+0H+SyFKncI1wOj1egiCAJ1Oh+joaG/vDhE+PFWD/7u3CvUt7abH3HkDcrZqc4AC6Pllm7NjpLFUybu4WmvzZq6pqrEa/ADAa9/3EllL4DWGXJsf614iyNb/fc/27si1ydx40Oaq80d/OatPsCjl+Gztr9Rrv7/9IO/xxP2bwQ+RB7ljfScp76fVt+LFD85YzT1SAIiLDsUfHpqI681tUEaFYVLyYJz4ukFW+TzOsnTT7b34q1HPm/n9qSpkbjxo9WZtvEmXPPtj3Pu7Q/22GxQRjAYL79ffdt1x8zcGMYDlXCdLQYwxaLJ1HvrbX6mzzNwZ/JFzPHH/5rAXkQc5O43dmfcLDw7o92b0/IKxmH5nrNnrmQQqnbWbrqXABzCv3xMVGiwpMXdb2WWb7aQGPj23646EX0cWT3U2QdmeWWbuXsSVfBuDHyKZ8NZK3p7u7fIGR5cJMd7My766Lqn91/U37d43KdyV8GtvnpyzCcqSi4o+MAbLpqf43XVI0jH4IfIh7g4UPL2St1wq6zq/TIi0858cE+HEe1jnzoRfe3o7nU2Ulho8xUaFMvCROQY/RD7CU4GCp4be5FRZ19mek4wRQ/B+xRWbCcI5GcPxn0cvWW1nL19bisQ45d/WebC2v+6ovk3+iXV+iHyAvy1qKbfKuo7eTI31e9LvGIIND6ZaDWiMU91DggKw4cFU02t7b8uY8CylT8MXSxcYp/wDlo8P6H9/nanGTvLC4IfIy/wxULAncdUfOFpYsnf9HilsFU18efE4ALYH0nx1KRJHC4oCzgdPJB8c9iLyMn8swS+3yro9C0s6whgAW9N7VXdbuVuWEtvjhTA8MmUYhsdG+HziuTO5ad5K7KeBhcEPkZf5Y6Agx9wL4033uT3mhSz7Y+9U954BcH+5W55ObHcHZ3LT/OH4yb0Y/BB5mT8GCs4mrg5UxmUs0gsPSFrM1t6p7vYEwJ6uKeVr5H781D/m/BB5mT8maco59yIkKAC/+ck4O/N/pLUeSAEwkS9j8EPkZf4aKDiTuDqQdRlECOEh+P+mD0dUmLTO9YwRQ/wuACbyZVzbi8hH+GtBQDlUeDay9H+oUADWPmV7rlVVXK21ey0sIn/EhU2dwOCHBiI5BQr+RuqCmkaWghp/DYCJ7MGFTYlkhkmaA5OUtb0CFEDPUk2Wpl67a5YSg2oicwx+iIicJGVtL4PYvaBmbFRovwGIqwNgS71JMZHBeGmhGvPGJ7jsfYgGEgY/REROsmdBzYUTb3fz3vzA2lBcfUsHntr5OX5+pREF81I9tj9EvoKzvYiInOSLtZqkDMVtOXIJH54aWOvGEbkCgx8iIif5Yq0mKUNxAPCr3afQ3mnwwB4R+Q4GP0RETvLFWk0HqrWS2ulbO5FeeACaKvYAkXxwqjuRFY7OkOn5utjIUEABXG9uM9tGe6cB28ou4+v6m0iOiUBOxnCEBAVY3U7v93d29k7P908aHI7Rqmhca2pDxTcNqNW3IiIkEKPjonC+rhk327swZfhgPD4tBQCs7ndzaydWv/s5Ll9vQUtbJzpFAwAFVFGhuNVhQEeXiKAAQBkdimFDInG1/iaOf90Ig8GAxMFhuC81HqFBgcgYMQTpd3Qn/JZ+eR3vV1zBzfZOTBk+BI9P636/nscfEx6Cs1o9Pvu6HhEhQVg4PgFfXm/B5evNqG1qBUQFbgsNRGqCgKHRYVBFWz+XMREhOKdtwrcNPxxfYIAC5V/dQNnFG+g0dKHxZgcu1DUDIjAoIghxQjiSYyKhAPDPi9dR8U0jmlo7TedaASA5JhxDbgvFnbG34dvGm9C3diI4EIiJCEVUeDCyRsfhlY+/wLXmNkSGBGFiYjQ6RAUmJgq4XN+CTy83IChAgXghDF/WNeFWWyfau0S0dYpQKIDEQWEYkxCF1k6gvrkN3zXewrVm28tr9LZq1gh8U38LzW0dEEVAoVDgttAg/DQtEdPujDW7xto7DXjj6EXs+fwq2ju7EBMRgtHx0Uge0n0urjTe6nONWLtu3TUbzbhdrb4V9c1tiIkMgTIqDJ1dBuyp/A5NrR1ouNmO8OAg3DE0Es/NS0V4SCButXfh1/urcfJKI24LC0TioAhcut6Cri4RXWIXaps6oBCB0fFRiA4LRkt7J260tKGxpQMhwQFYfHci/i3zjj5/0/3to7Vjl3JuLLUBgNIL1/H+51fQ0taJuOgwpA0bjPhB4ZiUPBgnvm6AVncL9S3tiLkttM/fhbP77QzW+XECgx9yhqP1Viy9rqd4IQzq26Px8dk6s2nPCgDzx6swO1UFZVQYGlra8OL+s2bbiQwJwJyxcahv6cCJbxrQ1Npl174ZFX5Yjdc/uWT2/o4KUAC5P0pB2cUbOPWd3vkNfi84QIEuUbS4j5OSB+HbGy2oc+DmbmQ8XwD6/f9SKICQwAC0cVgIAQogc8QQPJF5B/6r/DI+PndN0usUAOaqVYiLDsX/VHxnFhjGC2FYMCEeRSdrXF7bSFNVg+eLqqHV27cg8NDbQnCtWdrCtLb8fEZKvwnltj5npHwOWWozKCIYN9u7rA5n9i67YG3bju63sxj8OIHBDznK2gwZW5V27S1y5w5PTB+O2akqq9/CCj+sxpYjl7ywZ0TSOVvVWlNVgye/r5btbdYCIFufM8tnpOBvRy71+zkEwOWfOQr0f94d/Xy0B4MfJzD4IUd0GURkbjxovScAPyxH0Ltrur/XeZqlb2HtnQaMWv8Pq0stEPkSa39rtnQZREx6qRiNNx3vGXQlBYDzL801GwKT8jmjsNI7Y3xeJYRBFEVo9W0u319r593Rz0d7eeL+zYRnoh5szZARAdToWnH8Ur1dr/M0ra4VK7ZXmCWxvl16mYEPDRjW/tZsKf/qhs8EPkD3cWwru2z2mJTPmf6GpY3nxtWBT89tWzrvjn4++iIGP0Q9SC1W17ud1Nd5ivFz84V91ej6/lP008s3vLdDRA6y92+r7KLvXedf1980+93XPi8ssbSPjn4++iIGP0Q9OFqszpPF66Tq/S0sIpgF3Wngsf9vy/e6N5NjIsx+98XPi94s7aMvFvN0FIMfoh4cLVZnfJ0vMn4LG5PA3DcaOBwtDJlxR6x7dshBCgA5GcPNHpPyOdNfyozx3KiiQ61uw1H9nXdfLObpKAY/RD04WqwuMECBBROcn+LpDsZvYcqoUC/vCZE0zhSGTB8xBIMigl2/Uw5aPiOlT70fKZ8zuT9K6U58tvL8hgdT8fyCsRa34Sxr590Xi3k6yq7gp7CwEFOmTEFUVBSUSiUWLVqE8+fPm7VRKBQWf373u9+Z2rS1tSE/Px+xsbGIjIzEggULcOXKFbPtNDQ0ICcnB4IgQBAE5OTkoLGx0fEjJZIoWx2PzY+lQdWrJ0clhFmdxtllEFF00rcq5Pb+FqYSwr27Q0QSxUSGODxlOjBAgZcXj3P4vYfeFuLwa3vrr86Prc+ZgnmpNj+HrG1jUERwvwUWrcUm8f18xkndb1fU+fEEu6a6Z2dn45FHHsGUKVPQ2dmJf//3f8fp06dRXV2NyMhIAIBWa15S/R//+AeeeOIJfPnll7jjjjsAACtWrMC+ffvw1ltvYciQIVi7di3q6+tx4sQJBAYGAgDmzp2LK1eu4G9/+xsAYPny5Rg+fDj27dsnaV851Z2cZU8F07KLN/Do6+Uue+94IQxjE6Jx4GydQ6+3VHPD16bjk/eEBQWg1YcLN77y0AT8JC3RqW10Fzk8YzYjSggLwrLpw5GWNJgVnlnh2fHJr9euXYNSqURJSQlmzJhhsc2iRYvQ1NSEjz/+GACg0+kwdOhQbNu2DQ8//DAA4OrVq0hKSsKHH36IOXPm4OzZs0hNTUV5eTmmTp0KACgvL0dGRgbOnTuHUaNG2dw3Bj/kSXsrv8PTuypdsq31D4zBsukpKK7WYt17J9Hc1mXzNVFhgZIqPvtS8TdfFBqkAESgrcv3kmZd5eczUnCzvQvbyr9xyfZiIoNR3+LaqeXv5KYjY8QQp7fjzhs0uY8n7t9OTf/Q6XQAgJgYy8lNtbW12L9/P95++23TYydOnEBHRweysrJMjyUkJECtVqO0tBRz5sxBWVkZBEEwBT4AkJ6eDkEQUFpaajH4aWtrQ1vbDxG+Xu+6UvtEtrhidoOxQJgx8LGncusLD6oRPyjc5od8tjoeT0wfjjf+ednp/fVHbZ3Wz7gCvjiPyD4KAEUna/Bv01Ncsi2VEIb1D4zByp2fWz03+bPuxF8OfmnXNl2VMBsYoHBJEEX+x+GEZ1EUsWbNGmRmZkKtVlts8/bbbyMqKgqLFy82PabVahESEoLBgwebtY2LizMNmWm1WiiVyj7bUyqVfYbVjAoLC035QYIgICkpydFDI7KbrVkQUvVcb8qeG238oHBkjBiChRNvR8aIIf1+u52dqnJyL32Dp7+/q4Qw/Nv04R5+V9cylj8YrYrqdzaRLT2TW+eNT8Dmx9L6zHaMF8Lw2mNpeGb2XZL+NgZawiwNbA73/OTl5eHUqVM4evSo1TZvvvkmli5dirAw29+KRVGEQvHDBd/z39ba9FRQUIA1a9aYftfr9QyAyGOMsyBWbK9wqIdgSGQIfv0TNbLV8Si7eMOuvJwhkSF2fVM2BmpaXeuA68kYFB6Ml3/ancxqaTFHAC6v7jsoIhh/fTQN6SOG4PilerzpB71m9TfbkfujFIfXeVP1GlbNVsfj/lSV1SEmKX8bvbdJ5E4OBT/5+fkoKirCkSNHkJhoOSntk08+wfnz5/Huu++aPa5SqdDe3o6Ghgaz3p+6ujpMmzbN1Ka2trbPNq9du4a4uDiL7xcaGorQUE7lJe8xzoLofVO2toKyUUxkMMoK7jMlR9pbHXXhxARJ35R75j88MiUJrxy4YPVmNFetQtqwwfj1h2ft2hd3++vSNEy/s7uOi6WbLdBdgr+4Wov3TlwxW0HcUY03OxAQoEBggMJm4KgAMNgNOTBSdAfBg/GPqr6fnb0po8KwcN7tAIDXP7nU7/XZmzEnzVK5B2tDTNb+NmIig/GTibf3uxgvkTvYFfyIooj8/Hzs2bMHhw8fRkqK9XHjN954A5MmTcKECRPMHp80aRKCg4NRXFyMhx56CABQU1ODqqoq/Pa3vwUAZGRkQKfT4fjx47jnnnsAAMeOHYNOpzMFSES+yNI34IaWdqzc2Z1k3PMeY/yY/81PxpnNCrE3f+h+CcNYmqoau3pK/lGlxeffNGJQRDB0Nzu83kNkzAVJv+OHm6u1m23GiCHIGDEE4xIHYfW7lS55f2NA2l8Pn/H/86WFary4/6xHetaGRIZg4cQE3P998NBlEHGs8GPUt7RbbN87p6ZgXirWZo3GtrLLuHSjBXsrr6K5tdNqYGfMSXMkSLHVO0TkSXYFPytXrsTOnTuxd+9eREVFmfJvBEFAePgPNUT0ej3ee+89/OEPf+izDUEQ8MQTT2Dt2rUYMmQIYmJisG7dOowbNw6zZ88GAIwZMwbZ2dnIzc3Fli1bAHRPdZ8/f76kmV5E3mTpprw5oO+3Xmvd/MbeBalDXw0t/S9uqKmqsZg8rfs+6Jk/Ph4fnOpbo6hW7xvDYrZyQazN6FFFu67ids+A1FovRs//z4AAhcNDoFI8MX14n94SY4DbX+AD9D2PIUEBeOJH3WVIMu+M7TewczYfhwnI5CvsmupuLd9m69atWLZsmen3v/3tb3jmmWdQU1MDQRD6tG9tbcWzzz6LnTt34tatW7jvvvvw6quvmuXo1NfXY9WqVSgqKgIALFiwAJs2bcKgQYMk7SunupOvsWfarbWAxZJ4IQxHfznLamBgq7ZPf8NyCnT3EIUGBdi1gnRUWJDFIaeYyBCrN2drrE3bByz3aBnb35+qQubGg/0OUcVFhwJQWA30jL0dls6vrf9Pa/u2YEI8ik7WOFRvqb8SBraul/7OY+9tWTunzMchT/D5Oj++jMEPDXSaqho8t6dKUrBgrS6Kq4ov7vjZVAQoFPjwdA22lX9ts/0rD0+EKjqsTxG1ScmDce/vDtkMSP7w0ERcb27rN0i0dcN/7bE0AMCK7daHHDdLbOPoTd9agNTz8djIUEABvHH0Kxw6d63P8YQFBWDp1GFW82KkBLgxkcEoL5gtqehef/tN5Ak+X+eHiNwnWx2PW+1dWP3fJ222tZYkbW/ytDXXm9uwcGJ3gqyU4EcVHWZ1eMNWzszzC8aakpqt6TKINssB/Gr3aZz4v/fbHKICIKmNtf3oL0iwNsxj6fHpd8aivdOAt0sv49PL9YgICcRP707EtJGx/QYexy/V2+xFqm/pwImvGyQPOXF4ivwdgx8iHyZ1PS5rSdKuKL7YcztSZjvZKlInJWfGFik3/MabHdh08Es8PXukzURbR5Jx3TE8FBIUgNwZdyB3xh2SXyM1wHVVIEzkDxj8EPkwZ4MNKTV9AhSAKFpOzO29fSmznaQkxTo780fqjXxr6SXkzbpTUk+GPb0d1obctLpWrNhe4dEFHqUGuK4KhIn8gcMVnonI/YzBBtC3orGUYMPW6xUAcn+UYtf2XbWqszHYkFKVujepN/LGmx04fqle8nal6G/IzfjYC/uq0WVP8Zxe2y+7eAN7K79D2cUbNrdjq7q4At09Uq5aMoLIH7Dnh8jHOTtMJOX1dw8bbNf2vV2z5Z6UGAwKD0bjLdvFBF093GNryM24hMTxS/V25804MpTmqt44Ijlh8EM0ADgbbPT3+i6DCCE8BL/IHo365jbERIZAJYTb3L43k2IDAxT4/6YPxysHLths6+rhHnfl2DgzlOaKPCoiOWHwQzRAOBtsWHp9fz0NPQMfX5z6nDdrJLaWXra6lperVwg3ckeOja2hNAW6h9LuT1VZPe/e7o0jGkgY/BDJlNSeBnuHYjwVKAUGKPDy4nEWj8Gdwz2umPHW+xwZRNElQ2mcok4kDYMfIhmS2tNgMIhYufNzyUMxnq4O7I3hHmdzbCyusxYeLOm9OV2dyDVY4ZlIhqRWfo7pZ4Xy3ks/WOtJckWlZFu8MSznSKBnz7Illlir5E3kT1jhmYjcQmoPgrXABzAfirknJcbpnBVneGO4x94cGylVqa1xV/4SkVwx+CGSIVfOgKpranXr9G9fZk/QJaUqtSWcrk7keixySCRDUgrjDYkMkbQtZVQYl1iQQOqx987/sbd4JBHZxp4fIhmSkrT74kI1XtxfLWlWk9QqynJeYkHqsf91SRoCAhScrk7kRgx+iGRKykypgABImtXkiunf7uQLdYqknqN0O5f6ICL7cbYX0QDn7I3d1uulzmr68NRVPLXz8z7b98Rsr/54evq9rX1Zsb0CgOVgksNbRJ65fzP4IRrAPHVjdyRAcuf+SOXN6ff97ZMj/2e+0HtF5AkMfpzA4Id8mStuZN66sffe94aWdqzcab12zatL0jBvvOcDn/ZOA9ILD0iuU+QJxnOn1bfatY6apYBJFR2GR+8ZhuGxEQyGyK+wzg+RH3JFb02XQcTzRWc8XlfH0r4HKGA18FEAeHF/Neao7d+P9k4DtpVdxtf1N5EcE4GcjOEICZI2QVVTVYPn9lRJrlOUMWKI5IDU3v0ybvejM1r8T8UVNLV2mp6ztI5a7/d6bvcp/E/Fd32e0+pb8cqBL/psi8NmRLax54fIg/qr8KuA9N6aPx24YHbjs8aZisD29vA4sx893yv2tlDsPPY1PjytNXuvAAXwROZwzBqt6jdAsbeK8p8emYjQoABJPSu/1ZzF659cgqHHxgMUQO6PUlAwL7XPtvsbDjRSAPjrkrsxODLUdPwQgTf/+RUOnrtm9/lePXsk8maNZC8QDVgc9nICgx/yNV0GEZkbD/Z7IxwcEYzP/u/9Fm9cxgChuFqLN/95WdJ7vvLQBPwkLbHfXg1LzxVXay328Bgc/LT40yMTsXDi7RafkxIgWNO7t6N7qOtj1Le0S97GT+9OwPufX7XZLiIkEDfbu6w+n/ujFMwaHeeSYNFZqugwPL+AvUA0MDH4cQKDH/I1UtfTWj17JJ6efZdZUHL5+k28c/wbaPX2BQgxkSH4l0m3o+hkjcVhNgB9Ao+woAC0dhrseh9brPX8OLvWVc/8JgB4bs/pfoe6PMmZYNEVevYk9u5Zgwhcb2mDMioMk5IH48TXDUykJp/BnB8iPyK1wu/Wf17GSGUUXtzvWG9IT/Ut7dhy5FKfx7W6Vjz5/ZTr3lwd+MRbqe/jzFpXRsb8poLdp9Fw0zeCHiNvBj5GL+yrhsGAfq+l3kEac4dIDtjzQ+QhUnt+/E14kALzxicgIiQQADAxaTASBoXDIIpY+p/HvLx31BtrDpG3seeHaADqMogov3gDZV9dB9C98GX6HUNwT0oMBoUHo/GWb/VQuNutThHv95ittK38GwBAZAiXFvRFxm/Dz+05jVmj4yTPsCMaSNjzQ+RCmqoa/Gr3aTT2GoIZFBGMlxePw3lts6RZWkS+ICYyBL/5iZo9QORRnrh/M6QnchFNVQ2e3F7RJ/ABgMabHXhyewVGxEZaXUmdyNfUt7RjxfYKaKpqvL0rRC7F4IfIBYxFB2351Z6TXpn6TOSMF/ZVo+v7rOgug4iyizewt/I7lF28YXqcaCBhzg/5HHtr0gQGKLy+7lH3kgVtNts1t7l2JhWRu/WshK271e4zi8QaWau43TP3ThSBQREhiL1N2nIi5P8Y/JBP6W/pB6BvTZp4IQwLJsRbrGOz/oExpqq57g6IpE5jJ3K3n0xMwJ5K20Ub7VVcrcXWf17u03Op1bVixfYKt8wOs/WlpvDD6j4Vt1/68CzuG63EZ183WByCBrwfsJH3MeGZfEZ/C3W64iJ15weeXKexk+8wLtT6+3+Z4JYSAjGRIVYrZ7tjkVhba+AVflhtsYaVVPYsJ0OexYRn8itdBhH//PI6fv+/5/H7/z2Hf164bpZHYK3gnauic+M3VFcmbxrzH7S6WxgcHuyy7RI5YsODqUi/YwjihTCbifWDwoOxevZdOPv/shETGWK1nQJATGRwv0uG9BwacwXjF6HehRmNf8MfVF7F6584HvgA3fvcM5eJ5IXDXuQRlqaAbzp0EREhgfj5jBGYnDzY6WrGtrhitXPzJSdasPPYN6htsp3rQ+ROgyOCUbh4nKkXY8ODqVixvcJqr2nvxU9/8xM1Vnxf8btne+NfyE8m3o43JKwn54rhX1tfhBQAnvv7aZdU0DYGbI4u/ksDF4MfcjlLq4E/tdPyUgo327vwyoEvTNV/3c34DXX930/hZpsBLe2diL0tBM3tXWjr6MKU4UPw+LThAIA3P/kKuz//Du1dBoyIjUBgAHD0yxu42cFviuRbQoMCcH+qyvR7tjoemx9Lk5ycbK296vv2QniIpOBHGRXm9LEcv1Tf7xchEYC+tdPp9zFivp48Mfixk9QFAo3P1TW3ob65DTGR0mYZ9K4OPCV5ML6oa8a3DT/MZACAt0sv4dPLDYgMCcSiu29HUEAArre09dmncbcL2Kg5iy/rmvFt/U2EBwdi2JAIpCUOxsmrOoQHByIoUMSxrxrQ1mXA+PhoLM1IQX1LG/5RdRUXr91EcACQNDgCAYEBuC00CA+q43HoQh0u37iFsGAFRsTehuCgQExNicGJrxuwtfQydHZWMe5vtWx32Hn8isXHP6quw68/PNvn8cs3brp7l4gcptW39enByFbH4/5UleRZkP217zKIiBfCoNW1WuyRMeb8WFrDzV6eDkZcEbDRwGNXwnNhYSF2796Nc+fOITw8HNOmTcPGjRsxatQos3Znz57FL3/5S5SUlMBgMGDs2LH47//+bwwbNgwAMHPmTJSUlJi95uGHH8auXbtMvzc0NGDVqlUoKioCACxYsAB/+ctfMGjQIEn76o6EKUsJeD1JWcW5v6Rba9WBiYhs+dMjE7Fw4u1u274xDwewPDTmquRhqZMHXDERIt7FSdrkGj6X8FxSUoKVK1eivLwcxcXF6OzsRFZWFlpaWkxtLl68iMzMTIwePRqHDx/GyZMnsX79eoSFmUfXubm5qKmpMf1s2bLF7PklS5agsrISGo0GGo0GlZWVyMnJceJQnWMtAa8nKWPQNVaSbvurDkxEnhEXFdJv8q8vc3cPhnFoTCWYv49KCHPprKl7UmL6TdhWoDtoeWL6cKfeR4Hu3CgGPvLk1FT3a9euQalUoqSkBDNmzAAAPPLIIwgODsa2bdusvm7mzJmYOHEi/uM//sPi82fPnkVqairKy8sxdepUAEB5eTkyMjJw7ty5Pj1NlrgycuwyiMjceNClCbk9v3F0GURMf/ljSUXyiMg9jFOfAVgsueAOw4eE43pzO5rbHB/2dcc08/54oqCo1F6m3P/6FMXVdXZvn3V+fJvPr+qu0+kAADEx3eO8BoMB+/fvxy9+8QvMmTMHn3/+OVJSUlBQUIBFixaZvXbHjh3Yvn074uLiMHfuXGzYsAFRUVEAgLKyMgiCYAp8ACA9PR2CIKC0tFRS8ONKthLwHNFzloHU6sBE5B69b4abH0uzOQQ9f3w8PjhlX9mEnPRhUCgUfSoR984jrGtqxf/7oBoNEnuCPdmDERigcPvsKFsJ2Mb/p9f/dQo+qLyKgr+fRlOPJGhjkVMhPIQVnskih4MfURSxZs0aZGZmQq1WAwDq6urQ3NyMl19+GS+99BI2btwIjUaDxYsX49ChQ7j33nsBAEuXLkVKSgpUKhWqqqpQUFCAkydPori4GACg1WqhVCr7vKdSqYRWq7W4P21tbWhr+yGA0Ov1jh5aH+5KwDNul7MNiDwv78cjMDIuymLvhTH5d9PBC3jz6CXoetxYB0cE49eL1JijjseJrxusJgFbMnl4TJ+8HGvBRHhIoMXej578uQdDasL2/IkJmDs+3mq76SNjvbH75OMcDn7y8vJw6tQpHD161PSYwdC9btHChQuxevVqAMDEiRNRWlqK1157zRT85Obmml6jVqsxcuRITJ48GRUVFUhL6+52Vij6RuWiKFp8HOhOxn7hhRccPZx+uWss3bhdzjYg8rzpdw7ttwcjMECBp2ffhbxZI63eWI31dKSy52/dWu/HkMgQLJyYgPtTVX7fgyG1l8kTvVHkXxwKfvLz81FUVIQjR44gMTHR9HhsbCyCgoKQmppq1n7MmDFmQVJvaWlpCA4OxoULF5CWlgaVSoXa2to+7a5du4a4uDiL2ygoKMCaNWtMv+v1eiQlJdl7aBYZE/Ds+YZnS3yPaaH3pMRAFR3KoS/yCFV0KP7w0ERcb+4ujWAwiDh2qR6AiIw7YqG71YEX9/e94U5IElD5ra7fSr9GD45XYXaqCrG3hWLtf1dKvrYVCkBqFqLi+/b2Fruzd1p2fzdWY4DyfFE1tHrrPbiOTgW3d7o6EUljV/AjiiLy8/OxZ88eHD58GCkpKWbPh4SEYMqUKTh//rzZ41988QWSk5OtbvfMmTPo6OhAfHx3121GRgZ0Oh2OHz+Oe+65BwBw7Ngx6HQ6TJs2zeI2QkNDERoaas/hSBYYoLBZMdUevWcZBAYo8PyCsXjSjm+Q5HtctQaZOykAPL9gLKbfaT4U8KO7hpr9Pkdtvd6L8fHi6lp8eLrGLPgIUAC5P0pBwbwfvgA9v2CszQRi4638r4/eDSEiBGUXbwAQERigwJ8+/rLPuTW2z/1RCv525JLk8258nStzZHoOkb1y4ILL35O9GkSuZ9dsr6eeego7d+7E3r17zZKOBUFAeHg4AGDPnj14+OGH8de//hU//vGPodFo8Mwzz+Dw4cPIzMzExYsXsWPHDsybNw+xsbGorq7G2rVrER4ejk8//RSBgd2VfufOnYurV6+apsAvX74cycnJ2Ldvn6R9ZZ0f8pSI4AD8/N4RWDHzTpz4ugEfndHifyqumCVgBgcABgBdBvPXBiqALg9FTO7ID2nvNGBb2WV8XX/TLIm3N1t/O7b+LvqrVNzftnv/Tbo7R8bWvhKRbZ6Y7WVX8GMt32br1q1YtmyZ6fc333wThYWFuHLlCkaNGoUXXngBCxcuBAB8++23eOyxx1BVVYXm5mYkJSXhgQcewIYNG0yzxgCgvr6+T5HDTZs2ebXIIcAKz7YqPAcEKFCra8WnX9/AeW0zAhTA7DFxGJsg4HpzG+qa2nD2qh5Nre1ouNmB9i4RQngQfnTnUMQNCkdMeDD2n76KA2fr0GkwIDIkEMlDIjE8NhKiCJy52p3IPnn4IBy9cAP1Le2IDg/C3UlROHO1BR1dIgZHBOOu+Cgkx0Ti8rVmlFy4DkCEMioMN9s70dllQKBCgS4AwYEBSBwUjsAABSJCghASABy+cAOtHZ2Iiw7F0KgwNLV1oq2jCyGBARgcGYrpI4agub0TAYoAZIwYgvQ7hvT5P7U0HRiA2f+t8bXtnQb8ev8ZnLyigxAWjH9NH44jX9bh0o2biAgOxJyxKgyKCMGrhy6gRt+GBCEMby67B7eFBVmcJVTf0o6Y20KhvC0UUADXm9t8Yrikv78dKX8X/Q39GJ/X6lvN/t56/k166hx4Yio4kT/zueBnIPHEySMiIiLX8rkKz0REREQDHYMfIiIikhUGP0RERCQrDH6IiIhIVhj8EBERkaww+CEiIiJZYfBDREREssLgh4iIiGSFwQ8RERHJikOrug8ExsLVer3ey3tCREREUhnv2+5cgMJvg5+mpiYAQFJSkpf3hIiIiOzV1NQEQRDcsm2/XdvLYDDg6tWriIqKsrogq6/S6/VISkrCt99+y3XJ+sHzJA3Pk3Q8V9LwPEnHcyVNz/MUFRWFpqYmJCQkICDAPdk5ftvzExAQgMTERG/vhlOio6P5xyIBz5M0PE/S8VxJw/MkHc+VNMbz5K4eHyMmPBMREZGsMPghIiIiWWHw44NCQ0OxYcMGhIaGentXfBrPkzQ8T9LxXEnD8yQdz5U0nj5PfpvwTERERGQJe36IiIhIVhj8EBERkaww+CEiIiJZYfBDREREssLgxw0KCwsxZcoUREVFQalUYtGiRTh//rzV9j//+c+hUCjwH//xH2aPz5w5EwqFwuznkUceMWvT0NCAnJwcCIIAQRCQk5ODxsZGNxyV60k5T8uWLetzDtLT083atLW1IT8/H7GxsYiMjMSCBQtw5coVszYD+TwBrjtXvKa6nT17FgsWLIAgCIiKikJ6ejq++eYb0/P+fk256jz5+/UESDtXvc+B8ed3v/udqQ2vKWnnyWPXlEguN2fOHHHr1q1iVVWVWFlZKT7wwAPisGHDxObm5j5t9+zZI06YMEFMSEgQX3nlFbPn7r33XjE3N1esqakx/TQ2Npq1yc7OFtVqtVhaWiqWlpaKarVanD9/vjsPz2WknKfHH39czM7ONjsHN27cMNvOk08+Kd5+++1icXGxWFFRIf74xz8WJ0yYIHZ2dpraDOTzJIquO1e8pkTxyy+/FGNiYsRnn31WrKioEC9evCh+8MEHYm1tramNv19TrjpP/n49iaK0c9Xz+GtqasQ333xTVCgU4sWLF01teE1JO0+euqYY/HhAXV2dCEAsKSkxe/zKlSvi7bffLlZVVYnJyckWg5+nn37a6narq6tFAGJ5ebnpsbKyMhGAeO7cOVcegkdYOk+PP/64uHDhQquvaWxsFIODg8Vdu3aZHvvuu+/EgIAAUaPRiKLof+dJFB07V6LIa0oURfHhhx8WH3vsMauvkeM15ch5EkX5XU+iaP3zvKeFCxeKs2bNMv3Oa8qy3udJFD13TXHYywN0Oh0AICYmxvSYwWBATk4Onn32WYwdO9bqa3fs2IHY2FiMHTsW69atM61WDwBlZWUQBAFTp041PZaeng5BEFBaWuqGI3EvS+cJAA4fPgylUom77roLubm5qKurMz134sQJdHR0ICsry/RYQkIC1Gq16Rz423kCHDtXRnK+pgwGA/bv34+77roLc+bMgVKpxNSpU/H3v//d9Bo5XlOOnCcjOV1PgPW/PaPa2lrs378fTzzxhOkxXlN9WTpPRp64pvx2YVNfIYoi1qxZg8zMTKjVatPjGzduRFBQEFatWmX1tUuXLkVKSgpUKhWqqqpQUFCAkydPori4GACg1WqhVCr7vE6pVEKr1br+YNzI2nmaO3cu/uVf/gXJycm4dOkS1q9fj1mzZuHEiRMIDQ2FVqtFSEgIBg8ebLa9uLg40znwp/MEOH6uAF5TdXV1aG5uxssvv4yXXnoJGzduhEajweLFi3Ho0CHce++9srumHD1PgLyuJ8D6315Pb7/9NqKiorB48WLTY7ym+rJ0ngDPXVMMftwsLy8Pp06dwtGjR02PnThxAn/6059QUVEBhUJh9bW5ubmmf6vVaowcORKTJ09GRUUF0tLSAMDi60VR7He7vsjSeQKAhx9+2PRvtVqNyZMnIzk5Gfv37+/zR9NT73PgL+cJcO5cyf2aMhgMAICFCxdi9erVAICJEyeitLQUr732mummbom/XlPOnCc5XU+A9b+9nt58800sXboUYWFhNrcnp2uqN2vnyVPXFIe93Cg/Px9FRUU4dOgQEhMTTY9/8sknqKurw7BhwxAUFISgoCB8/fXXWLt2LYYPH251e2lpaQgODsaFCxcAACqVCrW1tX3aXbt2DXFxcS4/Hnexdp4siY+PR3Jystk5aG9vR0NDg1m7uro60znwl/MEOHeuLJHbNRUbG4ugoCCkpqaatR8zZoxpFpOcrilnzpMl/no9AdL+9j755BOcP38eP/vZz8we5zVlztp5ssRt15Tk7CCSzGAwiCtXrhQTEhLEL774os/z169fF0+fPm32k5CQIP7yl7/sN2Hr9OnTZglkxsSvY8eOmdqUl5cPmAQ5W+fJkuvXr4uhoaHi22+/LYriD4mE7777rqnN1atXLSYSDtTzJIquOVeWyPGaysjI6JPIu2jRIvHRRx8VRVEe15QrzpMl/nY9iaJ9f3uPP/64OGnSpD6P85oyZ+08WeKua4rBjxusWLFCFARBPHz4sNl0vZs3b1p9Te/ZXl9++aX4wgsviJ9++ql46dIlcf/+/eLo0aPFu+++u8/UyPHjx4tlZWViWVmZOG7cuAEzNdLWeWpqahLXrl0rlpaWipcuXRIPHTokZmRkiLfffruo1+tN23nyySfFxMRE8cCBA2JFRYU4a9Ysi1NIB+p5EkXXnCteU912794tBgcHi3/729/ECxcuiH/5y1/EwMBA8ZNPPjG18fdryhXnSQ7XkyhK/zzX6XRiRESEuHnzZovb4TXVrb/z5MlrisGPGwCw+LN161arr+kd/HzzzTfijBkzxJiYGDEkJEQcMWKEuGrVqj51W27cuCEuXbpUjIqKEqOiosSlS5eKDQ0N7jkwF7N1nm7evClmZWWJQ4cOFYODg8Vhw4aJjz/+uPjNN9+YbefWrVtiXl6eGBMTI4aHh4vz58/v02YgnydRdM254jX1gzfeeEO88847xbCwMHHChAni3//+d7Pn/f2acsV5ksP1JIrSz9WWLVvE8PDwPjVpjHhNdevvPHnymlJ8v9NEREREssCEZyIiIpIVBj9EREQkKwx+iIiISFYY/BAREZGsMPghIiIiWWHwQ0RERLLC4IeIiIhkhcEPERERyQqDHyIiIpIVBj9EREQkKwx+iIiISFYY/BAREZGs/P9AB3VlcXmiugAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "a=r2_score(y_test, y_pred)\n",
    "plt.scatter(y_test,y_pred)\n",
    "a"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyc_v\\AppData\\Local\\Temp\\ipykernel_19356\\2135086945.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_data['close8_pred']=val_y_pred\n",
      "C:\\Users\\zyc_v\\AppData\\Local\\Temp\\ipykernel_19356\\2135086945.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  val_data['close8_pred']=sc_y.inverse_transform(np.array(val_data['close8_pred'])[:,np.newaxis])[:,0]\n"
     ]
    },
    {
     "data": {
      "text/plain": "                     date    open    high     low   close    volume  \\\n8828  2023-01-09 11:15:00  2585.0  2589.0  2581.0  2585.0   55725.0   \n8829  2023-01-09 11:30:00  2584.0  2588.0  2568.0  2573.0  107029.0   \n8830  2023-01-09 13:45:00  2573.0  2583.0  2573.0  2578.0   61119.0   \n8831  2023-01-09 14:00:00  2578.0  2588.0  2578.0  2582.0   47084.0   \n8832  2023-01-09 14:15:00  2582.0  2589.0  2580.0  2587.0   34014.0   \n...                   ...     ...     ...     ...     ...       ...   \n9723  2023-03-10 14:45:00  2562.0  2563.0  2547.0  2547.0   77925.0   \n9724  2023-03-10 15:00:00  2548.0  2552.0  2538.0  2541.0  127371.0   \n9725  2023-03-10 21:15:00  2542.0  2551.0  2541.0  2548.0  106624.0   \n9726  2023-03-10 21:30:00  2548.0  2550.0  2545.0  2548.0   32998.0   \n9727  2023-03-10 21:45:00  2549.0  2564.0  2547.0  2560.0  107294.0   \n\n              adtm   RSI12   RSI20  RSI20_MA5  ...  noise_ratio  noise_ratio8  \\\n8828      0.000000  33.144  37.253    38.6922  ...    -0.006578      0.001557   \n8829      0.000000  28.303  33.690    37.8568  ...    -0.011079      0.003444   \n8830      0.000000  32.766  36.360    37.2316  ...    -0.009067      0.001126   \n8831   -886.761222  36.231  38.447    36.4948  ...    -0.007455      0.000734   \n8832   1285.502440  40.418  40.993    37.3486  ...    -0.005479      0.003011   \n...            ...     ...     ...        ...  ...          ...           ...   \n9723  -6072.305625  30.556  34.981    40.3178  ...    -0.019753     -0.013822   \n9724  -2317.624520  27.769  32.854    38.7532  ...    -0.021847     -0.018267   \n9725   9094.941901  35.283  37.520    38.2194  ...    -0.018965     -0.015416   \n9726    435.547202  35.283  37.520    36.8426  ...    -0.018780     -0.013738   \n9727  12189.179050  46.614  44.805    37.5360  ...    -0.014020     -0.010930   \n\n      ret_kalman_close8  dkalman30  ddkalman  volume_ma5  volume_ma10  \\\n8828          -0.000448   0.001495 -0.087460     75575.2      66247.0   \n8829          -0.000302   0.001343 -0.113465     65656.8      71630.8   \n8830          -0.000200   0.001246 -0.078179     67521.4      72256.8   \n8831          -0.000117   0.001168 -0.066184     68118.0      69065.4   \n8832          -0.000032   0.001042 -0.121221     60994.2      67042.3   \n...                 ...        ...       ...         ...          ...   \n9723          -0.001330  -0.003165  0.021920     47451.6      56181.8   \n9724          -0.001294  -0.003241  0.023584     61643.6      61960.4   \n9725          -0.001259  -0.003282  0.012536     75021.0      65880.0   \n9726          -0.001208  -0.003342  0.017925     73808.2      66075.9   \n9727          -0.001177  -0.003367  0.007334     90442.4      67065.3   \n\n       volume_ma30  close8  close8_pred  \n8828  61919.700000  2605.0  2657.815430  \n8829  64601.900000  2610.0  2652.832764  \n8830  64354.333333  2604.0  2654.478271  \n8831  64504.500000  2603.0  2655.881592  \n8832  62286.533333  2609.0  2657.113770  \n...            ...     ...          ...  \n9723  60737.066667  2559.0  2650.651123  \n9724  63226.666667  2547.0  2650.520508  \n9725  65185.233333  2554.0  2650.664551  \n9726  64713.433333  2558.0  2650.637939  \n9727  66683.833333  2565.0  2650.901611  \n\n[900 rows x 32 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>adtm</th>\n      <th>RSI12</th>\n      <th>RSI20</th>\n      <th>RSI20_MA5</th>\n      <th>...</th>\n      <th>noise_ratio</th>\n      <th>noise_ratio8</th>\n      <th>ret_kalman_close8</th>\n      <th>dkalman30</th>\n      <th>ddkalman</th>\n      <th>volume_ma5</th>\n      <th>volume_ma10</th>\n      <th>volume_ma30</th>\n      <th>close8</th>\n      <th>close8_pred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8828</th>\n      <td>2023-01-09 11:15:00</td>\n      <td>2585.0</td>\n      <td>2589.0</td>\n      <td>2581.0</td>\n      <td>2585.0</td>\n      <td>55725.0</td>\n      <td>0.000000</td>\n      <td>33.144</td>\n      <td>37.253</td>\n      <td>38.6922</td>\n      <td>...</td>\n      <td>-0.006578</td>\n      <td>0.001557</td>\n      <td>-0.000448</td>\n      <td>0.001495</td>\n      <td>-0.087460</td>\n      <td>75575.2</td>\n      <td>66247.0</td>\n      <td>61919.700000</td>\n      <td>2605.0</td>\n      <td>2657.815430</td>\n    </tr>\n    <tr>\n      <th>8829</th>\n      <td>2023-01-09 11:30:00</td>\n      <td>2584.0</td>\n      <td>2588.0</td>\n      <td>2568.0</td>\n      <td>2573.0</td>\n      <td>107029.0</td>\n      <td>0.000000</td>\n      <td>28.303</td>\n      <td>33.690</td>\n      <td>37.8568</td>\n      <td>...</td>\n      <td>-0.011079</td>\n      <td>0.003444</td>\n      <td>-0.000302</td>\n      <td>0.001343</td>\n      <td>-0.113465</td>\n      <td>65656.8</td>\n      <td>71630.8</td>\n      <td>64601.900000</td>\n      <td>2610.0</td>\n      <td>2652.832764</td>\n    </tr>\n    <tr>\n      <th>8830</th>\n      <td>2023-01-09 13:45:00</td>\n      <td>2573.0</td>\n      <td>2583.0</td>\n      <td>2573.0</td>\n      <td>2578.0</td>\n      <td>61119.0</td>\n      <td>0.000000</td>\n      <td>32.766</td>\n      <td>36.360</td>\n      <td>37.2316</td>\n      <td>...</td>\n      <td>-0.009067</td>\n      <td>0.001126</td>\n      <td>-0.000200</td>\n      <td>0.001246</td>\n      <td>-0.078179</td>\n      <td>67521.4</td>\n      <td>72256.8</td>\n      <td>64354.333333</td>\n      <td>2604.0</td>\n      <td>2654.478271</td>\n    </tr>\n    <tr>\n      <th>8831</th>\n      <td>2023-01-09 14:00:00</td>\n      <td>2578.0</td>\n      <td>2588.0</td>\n      <td>2578.0</td>\n      <td>2582.0</td>\n      <td>47084.0</td>\n      <td>-886.761222</td>\n      <td>36.231</td>\n      <td>38.447</td>\n      <td>36.4948</td>\n      <td>...</td>\n      <td>-0.007455</td>\n      <td>0.000734</td>\n      <td>-0.000117</td>\n      <td>0.001168</td>\n      <td>-0.066184</td>\n      <td>68118.0</td>\n      <td>69065.4</td>\n      <td>64504.500000</td>\n      <td>2603.0</td>\n      <td>2655.881592</td>\n    </tr>\n    <tr>\n      <th>8832</th>\n      <td>2023-01-09 14:15:00</td>\n      <td>2582.0</td>\n      <td>2589.0</td>\n      <td>2580.0</td>\n      <td>2587.0</td>\n      <td>34014.0</td>\n      <td>1285.502440</td>\n      <td>40.418</td>\n      <td>40.993</td>\n      <td>37.3486</td>\n      <td>...</td>\n      <td>-0.005479</td>\n      <td>0.003011</td>\n      <td>-0.000032</td>\n      <td>0.001042</td>\n      <td>-0.121221</td>\n      <td>60994.2</td>\n      <td>67042.3</td>\n      <td>62286.533333</td>\n      <td>2609.0</td>\n      <td>2657.113770</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9723</th>\n      <td>2023-03-10 14:45:00</td>\n      <td>2562.0</td>\n      <td>2563.0</td>\n      <td>2547.0</td>\n      <td>2547.0</td>\n      <td>77925.0</td>\n      <td>-6072.305625</td>\n      <td>30.556</td>\n      <td>34.981</td>\n      <td>40.3178</td>\n      <td>...</td>\n      <td>-0.019753</td>\n      <td>-0.013822</td>\n      <td>-0.001330</td>\n      <td>-0.003165</td>\n      <td>0.021920</td>\n      <td>47451.6</td>\n      <td>56181.8</td>\n      <td>60737.066667</td>\n      <td>2559.0</td>\n      <td>2650.651123</td>\n    </tr>\n    <tr>\n      <th>9724</th>\n      <td>2023-03-10 15:00:00</td>\n      <td>2548.0</td>\n      <td>2552.0</td>\n      <td>2538.0</td>\n      <td>2541.0</td>\n      <td>127371.0</td>\n      <td>-2317.624520</td>\n      <td>27.769</td>\n      <td>32.854</td>\n      <td>38.7532</td>\n      <td>...</td>\n      <td>-0.021847</td>\n      <td>-0.018267</td>\n      <td>-0.001294</td>\n      <td>-0.003241</td>\n      <td>0.023584</td>\n      <td>61643.6</td>\n      <td>61960.4</td>\n      <td>63226.666667</td>\n      <td>2547.0</td>\n      <td>2650.520508</td>\n    </tr>\n    <tr>\n      <th>9725</th>\n      <td>2023-03-10 21:15:00</td>\n      <td>2542.0</td>\n      <td>2551.0</td>\n      <td>2541.0</td>\n      <td>2548.0</td>\n      <td>106624.0</td>\n      <td>9094.941901</td>\n      <td>35.283</td>\n      <td>37.520</td>\n      <td>38.2194</td>\n      <td>...</td>\n      <td>-0.018965</td>\n      <td>-0.015416</td>\n      <td>-0.001259</td>\n      <td>-0.003282</td>\n      <td>0.012536</td>\n      <td>75021.0</td>\n      <td>65880.0</td>\n      <td>65185.233333</td>\n      <td>2554.0</td>\n      <td>2650.664551</td>\n    </tr>\n    <tr>\n      <th>9726</th>\n      <td>2023-03-10 21:30:00</td>\n      <td>2548.0</td>\n      <td>2550.0</td>\n      <td>2545.0</td>\n      <td>2548.0</td>\n      <td>32998.0</td>\n      <td>435.547202</td>\n      <td>35.283</td>\n      <td>37.520</td>\n      <td>36.8426</td>\n      <td>...</td>\n      <td>-0.018780</td>\n      <td>-0.013738</td>\n      <td>-0.001208</td>\n      <td>-0.003342</td>\n      <td>0.017925</td>\n      <td>73808.2</td>\n      <td>66075.9</td>\n      <td>64713.433333</td>\n      <td>2558.0</td>\n      <td>2650.637939</td>\n    </tr>\n    <tr>\n      <th>9727</th>\n      <td>2023-03-10 21:45:00</td>\n      <td>2549.0</td>\n      <td>2564.0</td>\n      <td>2547.0</td>\n      <td>2560.0</td>\n      <td>107294.0</td>\n      <td>12189.179050</td>\n      <td>46.614</td>\n      <td>44.805</td>\n      <td>37.5360</td>\n      <td>...</td>\n      <td>-0.014020</td>\n      <td>-0.010930</td>\n      <td>-0.001177</td>\n      <td>-0.003367</td>\n      <td>0.007334</td>\n      <td>90442.4</td>\n      <td>67065.3</td>\n      <td>66683.833333</td>\n      <td>2565.0</td>\n      <td>2650.901611</td>\n    </tr>\n  </tbody>\n</table>\n<p>900 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data=data.iloc[-900:]\n",
    "val_x=val_data[['noise_ratio','ATR10','CCI14','MACD','MTM10','ROC10','STD26','volume_ma10','dkalman30','ddkalman','volume_ma30','RSI20_MA5']].values\n",
    "x_topre= sc_X.transform(val_x)\n",
    "x_topre=x_topre[:,:,np.newaxis]\n",
    "val_y_pred = model.predict(x_topre)\n",
    "val_data['close8_pred']=val_y_pred\n",
    "val_data['close8_pred']=sc_y.inverse_transform(np.array(val_data['close8_pred'])[:,np.newaxis])[:,0]\n",
    "val_data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 3000x1000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.plot(val_data['close8_pred'].shift(),color='blue')\n",
    "plt.plot(val_data['close8'],color='red')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
